Transforming Afghanistan’s Mining Sector through AI and Digital Technologies

About the Author

He is an AI and machine learning scientist with over 15 years of experience, from leading Afghanistan’s first World Bank-funded e-procurement system to building AI solutions at Deutsche Bank. He specialises in data science, MLOps, business analytics, generative AI, large language models (LLMs), AI compliance and automation, as well as cloud and data architecture. He helps organisations build smart, scalable AI solutions that drive real-world impact.

Assessment of the Current Mining Landscape in Afghanistan

 Afghanistan is endowed with a wide array of mineral resources distributed throughout its rugged terrain. Estimates by the Afghan Ministry of Mines and U.S. surveys have valued Afghanistan’s mineral wealth at $1–3 trillion USD in potential, including sizeable reserves of copper, iron ore, gold, cobalt, lithium, rare earth elements, chromite, lead, zinc, uranium, coal, marble, and gemstones ². For example, surveys report about 60 million tonnes of copper (primarily at the Mes Aynak deposit in Logar province) and 2.2 billion tonnes of iron ore (e.g. the Hajigak deposit in Bamyan) in Afghanistan – together worth hundreds of billions of dollars at current prices ². Significant rare-earth element deposits (≈1.4 million tonnes) have been identified ², and an internal U.S. memo once described Afghanistan as the “Saudi Arabia of lithium” due to its lithium potential ². Precious and semi-precious stones are also prominent: Afghanistan has been a major source of lapis lazuli (mined for millennia in Badakhshan), emeralds (Panjshir), rubies, and others ². Most provinces harbour some mineralization, from copper and tin in Herat, to rare earths in Helmand, to talc and nephrite jade in Nangarhar, to coal in Baghlan and Takhar. However, despite this geological richness, the vast majority of these resources remain untapped or only informally extracted, contributing little to official revenues.

The mining sector in Afghanistan has long been characterised by informality, weak regulation, and corruption, which have impeded systematic development. During the past two decades, mining was often controlled by local strongmen or insurgent networks rather than the state. Illegal and artisanal mining proliferated – one 2017 report described the exploitation as “large-scale looting,” noting that many mines were run outside the law, with profits fuelling conflict and insurgent groups ¹. Taliban insurgents and other non-state actors derived substantial income from minerals (for instance, a 2018 Global Witness investigation found the Taliban and ISIS-K earned millions from Nangarhar’s talc mines) ¹. Even within the former government (2001–2021), corruption was rampant: although a mining law in 2010 forbade officials from holding concessions, by 2015 over 50 members of parliament reportedly owned mines or had partnerships in mining projects ¹. Smuggling and off-book sales have been endemic – for example, the majority of gemstones (like lapis lazuli from Badakhshan) are mined artisanally and leave the country illegally via illicit trade routes (often into Pakistan), depriving Afghanistan of vital revenues ². In summary, governance of the extractive sector has been extremely weak, with few enforced standards and much of the activity happening in the shadows of the informal economy.

Another major constraint on mining has been limited infrastructure and data. Afghanistan’s rugged geography and decades of conflict mean there are scarce transportation links (few railways, poor roads) and insufficient power supply to support large-scale mining operations ³. The country is landlocked with long supply chains to seaports, raising costs. A lack of detailed, modern geological data also hampers development – much of the available resource data originated from Soviet-era studies, with only partial updates from U.S.-led surveys in the 2000s ³. (Notably, the U.S. Geological Survey undertook extensive aerial hyper spectral scans and released 60 high-tech mineral maps covering ~70% of Afghanistan in 2014 ⁴ , identifying numerous promising mineralized zones, but this data has not been fully leveraged on the ground.) Basic infrastructure such as modernised mine sites, processing facilities, and labs are largely absent. Institutional capacity in the Ministry of Mines has historically been low – despite nearly $963 million USD spent by the U.S. (2004–2021) on Afghan mining sector training, surveys, and regulatory support, those efforts “failed to meet their goals” and yielded limited sustainable capacity ¹. Human capital in geology, engineering, and regulatory enforcement remains thin.

Since the Taliban takeover in August 2021, the political context has shifted, but the mining sector remains a crucial economic “cash cow” for Afghanistan’s de facto authorities ¹. The Taliban-led Ministry of Mines and Petroleum has moved aggressively to formalise some operations (at least on paper): by early 2024, they had issued over 200 mining contracts to more than 150 companies – an average of >1 new license per week ¹. These deals include foreign partnerships (with investors from China, Iran, Turkey, Qatar, etc.) as well as Taliban-linked enterprises ¹. The Taliban ministry has even taken direct equity stakes in certain projects, indicating a hands-on approach ¹. On one hand, this rapid contracting could mark a step toward regularising the sector; on the other, concerns of opacity and cronyism persist. Fewer than 10% of the companies now holding mine contracts had licenses under the former government, suggesting an entirely new network of operators close to the Taliban regime ¹. Reports already indicate that corruption and patronage remain prevalent under Taliban rule – lucrative concessions may be awarded to those with the right connections or for kickbacks ¹. Enforcement of labor, safety, and environmental rules is still minimal, and illegal mining continues in regions beyond the Taliban’s immediate reach ^{13 3}. In short, Afghanistan’s current mining landscape is one of huge potential but profound governance challenges. Any transformation toward a sustainable, regulated industry will have to address this legacy of informality and limited capacity.

Viable AI Technologies for Low-Infrastructure Settings

 

Afghanistan’s constraints – from limited connectivity and power to a shortage of funds and experts – mean that any AI and digital solutions must be pragmatic, low-cost, and robust in low-resource environments. Fortunately, a range of open-source and lightweight AI tools are available that can be adapted for use by governments with minimal infrastructure. Unlike proprietary enterprise systems, open-source AI libraries (such as TensorFlow, PyTorch, or Scikit-learn) can be deployed on standard computers and even on mobile devices at little or no licensing cost. Pre-trained models (for example, for image recognition or anomaly detection) can be obtained freely and run offline or on modest hardware. This is critical for Afghanistan, where budgets are tight and access to high-end hardware or cloud computing may be restricted. By leveraging open-source software and commodity hardware (like standard laptops or inexpensive single-board computers), Afghan institutions can experiment with AI solutions without large upfront investment. There are already examples of resource-constrained governments successfully using open-source AI – for instance, several African countries have adopted open GIS and machine-learning tools in agriculture and disaster management, proving that cost-effective AI adoption is feasible even in low-income contexts ⁶.

Mobile-based platforms offer another vital opportunity in low-infrastructure settings. Mobile phone penetration in Afghanistan is far higher than broadband internet access; even if only ~18% of Afghans use the internet regularly ¹⁴, a majority have access to mobile networks (via basic cell phones or smartphones). This makes mobile devices a potent platform for deploying digital services. AI-driven applications can be delivered through smartphones or even via SMS/voice for feature phones. For example, officials or citizens in remote mining areas could use mobile apps to collect data (reporting local mining activity, incidents, or environmental conditions), which can then be aggregated for analysis. Lightweight AI models (such as TensorFlow Lite models) can even run on-device for tasks like translating local language reports or categorising field photos, reducing the need for constant connectivity. Cloud services, when available, can complement this by providing heavy compute power off-site: data collected via mobile can sync to cloud servers when a connection is present, where more complex AI analysis is performed. Importantly, many cloud providers offer “AI as a Service” and open data programs that can benefit low-resource users. For example, Amazon’s Sustainability Data Initiative hosts free satellite imagery and climate data on the cloud, and its Digital Earth Africa platform provides African countries with ready-to-use Earth observation data for natural resource management. This has directly helped governments – “providing decision-ready insights” and even enabling Ghanaian officials to identify and tackle illegal mining using satellite data made freely available in the cloud ⁶. Afghanistan could similarly piggyback on such services (accessing satellite imagery or AI algorithms hosted by international partners) instead of building costly systems from scratch.

Several case studies illustrate how conflict-affected or low-infrastructure regions can implement digital technologies in governance. In West Africa, for instance, Liberia and Sierra Leone have used mobile data collection and open-source mapping to monitor diamond mining and ensure compliance with the Kimberley Process. In Ghana, as noted, an open Earth observation system combined with simple machine-learning analysis has bolstered the fight against illegal gold mining ⁶. In the Amazon rain forest – another environment with limited on-the-ground infrastructure – journalists and NGOs launched an AI-powered platform to monitor mining remotely: the Amazon Mining Watch system analyses satellite imagery across 6.7 million km² to automatically detect signs of gold mining in the jungle ⁵. This was achieved through a partnership of nonprofits using open algorithms and crowd-sourced training data, not a government mega-project ⁵. The platform runs analyses on millions of high-resolution image patches every few months and flags areas of likely mining activity, providing an example of how AI can scale monitoring in data-sparse, difficult terrains ⁵. Such models and codebases are openly published  ⁵, meaning Afghan stakeholders could study and adapt them for local monitoring of remote provinces. The common thread in these examples is the use of innovative, low-cost solutions: rather than require extensive new physical infrastructure, they leverage existing data (satellite images, SMS networks) and free or shared technologies. By following similar approaches – using mobile phones as sensors, satellites as eyes, and cloud-based or open-source AI as the analytical engine – Afghanistan can begin reaping the benefits of digital transformation in mining governance even with its current limitations. International partners (UN agencies, NGOs, tech firms) can assist by providing access to cloud platforms or training on open-source AI tools, ensuring that solutions are affordable and locally manageable.

 

AI in Mineral Exploration and Extraction

 

Applying artificial intelligence to mineral exploration could significantly improve Afghanistan’s ability to discover and quantify its mineral resources, despite a lack of advanced equipment on the ground. Traditional prospecting methods often rely on sparse geological mapping and manual interpretation, but AI excels at finding patterns in complex geodata that humans might miss ¹⁰. One immediate opportunity is to leverage the troves of geophysical and remote sensing data already collected (e.g. the USGS aerial hyperspectral surveys) by running machine-learning algorithms to identify subtle signatures of mineralization. For instance, algorithms can analyse multi-spectral satellite images or airborne geophysical scans to detect “anomalies” in surface chemistry or magnetics that suggest underlying mineral deposits ⁴. In fact, U.S. scientists previously used such data (ASTER satellite imagery and HyMap spectrometer data) to map surface mineral concentrations across Afghanistan, pinpointing areas with high potential for copper, lithium, rare earths, and other deposits ⁴. Today’s AI techniques (such as deep learning classification) can enhance these efforts by training on known deposit locations and then predicting other areas with similar geological signatures. The use of predictive modeling for reserve estimation is also promising: by inputting known drilling results and geological features, machine learning models can help estimate the size and grade of ore bodies more accurately and rapidly than conventional methods. A notable example in industry is Gold spot Discoveries, a company that uses AI to integrate geology, geochemistry, and geophysics data to predict likely gold deposit locations – they have successfully identified new target zones that were later confirmed by drilling ¹⁰. Afghanistan’s Geological Survey, with support, could partner with such AI exploration initiatives to maximise the insights from limited exploration programs.

AI can also play a role in improving mineral extraction processes, although full automation may be a longer-term prospect for Afghanistan. In large-scale mining operations globally, AI-powered systems are already increasing efficiency and safety – from autonomous drilling rigs and haul trucks to real-time equipment health monitoring. For example, at some modern mines in Australia, self-driving trucks and trains (guided by AI algorithms and sensor fusion) move ore with minimal human intervention, reducing accidents and costs ¹⁰. While Afghanistan’s current mining is mostly small-scale, over time the introduction of such technology could be transformative. Even in the nearer term, AI-driven decision support can help optimise whatever machinery and techniques are in use. This might include simple implementations like predictive maintenance systems that monitor vibrations or engine metrics on generators and excavators, alerting operators of potential breakdowns before they happen – preventing costly downtime. Machine learning models can also advise on extraction by analysing sensor data from mining equipment to suggest optimal settings (for example, drilling pressure or crusher speeds) tailored to the local ore conditions, thus squeezing more productivity out of existing equipment. Additionally, AI can assist with 3D modelling of deposits and mines: by interpolating between sparse drill holes and surveying data, an AI model can help create a more accurate three-dimensional map of an ore body and surrounding geology. This is crucial for planning efficient and safe mine layouts. Using such a model, miners can decide where to mine first, how to minimise waste rock removal, and how to avoid geotechnical hazards. In Afghanistan, where detailed ground surveys are few, an AI-enhanced 3D model based on limited new drilling plus legacy Soviet data could quickly give a clearer picture of major deposits, informing better investment decisions.

It is important to note that AI is not a substitute for fundamental fieldwork – exploration still requires drilling, sampling, and geological expertise on the ground. However, AI serves as a force multiplier: it can guide geologists on where to focus limited resources by ranking the most promising zones and by extracting maximum information from the data they do have. By adopting AI tools for exploration, Afghanistan could fast-track the discovery of new mineral zones and better quantify known ones, even with a modest exploration budget. Early pilot projects could include using remote sensing AI to map artisanal mining areas (to understand what minerals local prospectors are finding) and to conduct “digital drilling” – i.e. using algorithms on existing geophysical datasets to predict deeper mineralization before physical drilling is done. As for extraction, once any mid-sized formal mining operations begin (for example, the Mes Aynak copper project if revived), AI-based technologies for mine operations could gradually be introduced in partnership with experienced international companies. This might start with basic steps like equipping haul trucks with fatigue monitoring AI systems (as companies like BHP have done – using smart caps that detect driver drowsiness and have “experienced zero accidents due to drowsiness” after deployment ¹⁰) or using drones with AI to survey mine pits for better mine planning. Over time, these technologies improve productivity and safety, creating a more modernised mining sector. In summary, AI can assist Afghanistan from the very start of the mining value chain (exploration and resource modelling) through to operational optimisation, helping to overcome the country’s current lack of detailed data and experience.

AI for Regulation, Transparency, and Monitoring

 

One of the most potent applications of AI and digital tech in Afghanistan’s mining sector is to strengthen regulation and oversight, plugging the gaps that allow corruption and illegality to thrive. A first step is establishing digital systems to track mining licenses, contracts, and production – essentially creating an electronic mining cadastre and database. By digitising all mine licenses and permits and then applying automated checks, authorities can more easily flag irregularities. For example, an AI-driven system could cross-verify if any two licenses overlap geographically (preventing double allocation of the same area) or if mining is occurring outside the licensed boundaries by comparing reported coordinates with satellite imagery. It can also automatically monitor license timelines (alerting when a license is expiring or when certain conditions for renewal are unmet). Such systems greatly reduce opportunities for officials to manipulate records or for companies to evade rules, since all data is logged and auditable. An illustrative case comes from India, where some states use a centralised digital permit system for mining and employ simple analytics to detect if declared volumes of extracted minerals exceed the allowed quota. Afghanistan’s Taliban administration has already put some information online – the Ministry’s Transparency Portal lists contracts – but an interactive, AI-enhanced database could go further in ensuring compliance in real time.

Perhaps the most dramatic gains can come from AI-driven monitoring of mining activities via satellite and drone imagery. With so many remote and rugged areas, it’s infeasible for Afghan regulators to physically inspect all mining sites. Instead, AI can act as an ever-watchful “eye in the sky.” Modern satellite imagery (from programs like EU’s Sentinel or commercial providers) can detect changes on the Earth’s surface at frequent intervals. By training an AI model to recognise the characteristic signs of mining – e.g. the bare-earth pits and tunnelling of gold placer mines, or the discoloration from tailings – authorities can automatically spot illegal or unlicensed mining operations from space. This approach is already used in the Amazon rain forest, where an AI algorithm scans massive satellite datasets and has identified over 6,800 km² of likely mining areas, flagging them for investigators ⁵. The AI doesn’t alone enforce the law, but it provides a map of hotspots where action is needed ⁵. Afghanistan could deploy a similar system: for instance, an AI model could routinely analyse imagery of known mineral-rich districts (like parts of Nangarhar, Badakhshan, Ghazni, etc.) and highlight any new excavation scars or mining infrastructure that appear, indicating possible illegal extraction. When suspicious sites are detected, drones can be sent (where accessible) for closer aerial inspection, using computer vision to assess activity (number of workers, equipment, etc.). Drone-based surveillance with AI is especially useful to monitor active licensed mines as well – ensuring they are following environmental and safety rules. An AI algorithm could, for example, compare the size of a mine’s pit over time against the company’s reported production; a significantly larger pit with low reported output might suggest under reported production or smuggling.

To improve transparency in mineral supply chains, emerging technologies like blockchain can be leveraged in tandem with AI. Blockchain-based digital ledgers allow the tracking of minerals from the mine to the point of export in a tamper-proof way. Each batch of ore or gemstones can be tagged (with QR codes or digital tokens) and logged on a blockchain at each transaction – creating an immutable chain of custody. This could combat the chronic problem of minerals being sneaked out or mixed with contraband. For instance, a pilot project by a consortium of tech and automotive companies recently used blockchain to trace cobalt from a DRC mine all the way through the supply chain to a Ford electric vehicle plant, with each participant (miners, smelters, battery makers) recording data to ensure the cobalt was sourced responsibly ⁷. Afghanistan could implement a similar system for high-value resources like rare earths or lithium (should large-scale production commence), or even for gemstones to certify they are conflict-free. Blockchain records, combined with AI analysis, can also help detect fraud or discrepancies. For example, machine learning could analyse export records and the blockchain logs to find anomalies – if the volume of a mineral declared at the border is far less than what satellite imagery and mine data suggest was produced, this discrepancy would be flagged for investigation. Likewise, AI could scan financial transaction patterns associated with mining companies and identify red flags (like payments to politically exposed persons or sudden spikes in revenue that don’t match production). By bringing data together from various sources – satellite images, production logs, export databases, and even customs scanners – and analysing it holistically, AI can uncover the “invisible” relationships that often indicate corruption or smuggling networks.

Another area is the use of AI-powered e-government platforms to reduce face-to-face interactions (which often breed rent-seeking). For instance, an online licensing portal where companies apply for mining permits and upload required data can incorporate an AI assistant to verify documents and even evaluate the applications against objective criteria (like technical qualifications, work plan soundness). This minimises discretionary power of officials and ensures more standardised decisions. Natural language processing (NLP) AI tools could also be used to sift through large volumes of unstructured data – say, contracts, environmental impact assessments, or company reports – to check compliance and consistency. An NLP system might scan all mining contracts to extract key terms (royalty rates, ownership, dates) and then cross-check if those terms are being adhered to or if any clauses look unusual compared to the norm (which could indicate a sweetheart deal). Essentially, AI can act as a tireless auditor, reviewing paperwork and data that human regulators don’t have the capacity to analyse in depth.

In summary, by deploying AI for monitoring and transparency, Afghanistan can greatly augment its regulatory reach. Even with few boots on the ground, an AI system scanning satellite data or crunching numbers can watch over vast areas and large datasets, instantly pinpointing where human attention is needed. Such an approach would deter illegal mining (knowing that “someone is watching” even in remote mountains) and deter corrupt behaviour (since data discrepancies will be caught). Importantly, it also provides credible information to the public and international partners – for example, publishing an online dashboard of mining activities detected and actions taken can build confidence in Afghan minerals being managed responsibly. Over time, these digital oversight mechanisms will help transition the sector from today’s opacity to a culture of open, rule-based governance.

Environmental and Social Safeguards through AI

 

Ensuring that mining in Afghanistan develops sustainably will require robust environmental and social safeguards – areas where AI can offer valuable tools to predict risks and improve oversight. One application is predictive modelling for environmental risk assessment. Afghanistan’s terrain is prone to natural hazards that mining can exacerbate, such as landslides in mountainous areas or contamination of scarce water sources. AI systems can analyse geological, topographical, and meteorological data to forecast potential hazards around mining sites. For instance, a machine learning model could be trained on data about slope angles, soil types, and rainfall patterns to predict which mine waste dumps or tailings ponds have a high risk of slope failure or landslides, enabling preventative action before the rainy season ⁹. Similarly, AI could help model the dispersion of pollutants: given data on wind patterns and river flows, an AI-based simulation might predict how dust from a quarry or effluent from a mine could spread, identifying communities or ecosystems at risk. This allows regulators to mandate protective measures (like retaining walls or water treatment) in advance. In major mining countries, companies are starting to use AI for tailings dam monitoring – feeding sensor data (pore pressure, vibration readings, etc.) into algorithms that can warn of instability in a dam holding mine waste ^{15 10}. In Afghanistan, large tailings facilities might not exist yet, but as mining grows, such AI-enhanced monitoring (possibly combining satellite InSAR data to detect ground movement, with on-site sensors) will be crucial to avoid disasters like dam collapses or toxic leaks.

AI can also contribute to ongoing environmental monitoring during mining operations. Satellite imagery with AI analysis can keep track of environmental indicators such as vegetation cover, river sediment levels, or air quality (e.g. detecting dust plumes) around mining areas. By comparing images over time, AI can quantify the footprint of mining – how much forest has been cleared, how far mine runoff has travelled downstream, etc. If a mine is supposed to rehabilitate land concurrently, AI-aided image analysis can check if new vegetation is indeed regrowing on mined-out plots. This provides an independent check on environmental compliance. Likewise, ground-level data like water quality measurements can be fed into an AI system to look for concerning trends (for example, rising heavy metal concentrations in groundwater near a mine). If an anomalous spike is detected, an alert can be issued instantly, whereas traditional monitoring might only catch such issues at infrequent inspection intervals. These data-driven approaches ensure that environmental damage is caught early and that companies are held accountable to mitigation commitments.

On the social side, AI can help protect labor rights and community well-being in the mining sector, which is especially pertinent given Afghanistan’s history of artisanal mining with poor safety and use of child labor in some areas. One innovative approach is using mobile reporting and AI analytics to monitor labor conditions. Workers and community members can be empowered with simple mobile tools (SMS surveys, Whats App chat bots, etc.) to report issues anonymously – for example, reporting if they see children working at a mine, if wages are unpaid, or if unsafe conditions exist. These incoming reports (which could be thousands of messages) can be processed by an AI system employing natural language processing to detect patterns and red-flag keywords. An example of this in practice is the platform developed by Ulula, which won an International Labour Organisation award for using a “bottom-up” data approach: it gathers multi-channel mobile survey data from workers and communities and then uses an AI tracker to predict and identify child labor and forced labor risks, piloted in mining areas of the DRC ⁸. The system can adapt to local languages and context, and it proved that even in low-tech environments, combining “mobile-based impact monitoring solutions” with AI analytics can spotlight labor abuses that would otherwise remain hidden ⁸. Adopting a similar approach in Afghanistan could help authorities and NGOs keep an eye on abusive labor practices in mines – essentially crowd sourcing the detection of violations. For instance, if multiple independent reports from a province mention children in a certain mining site, the AI can flag this for urgent investigation by the Ministry or humanitarian agencies.

Computer vision AI is another tool that could enhance safety: AI algorithms can be applied to photographs or video footage from mine sites (say, taken by inspectors or drones) to automatically check for visible safety compliance, like whether workers are wearing helmets and protective gear, or to count the number of workers and identify if any appear underage. In industrial mines globally, some companies deploy AI cameras that detect if workers are in unauthorised zones or not using safety equipment, immediately notifying supervisors. In Afghanistan’s context, this could even be done via periodic drone flyovers or remote cameras at larger mines, given that inspector visits might be rare in insecure areas. The AI would serve as an unbiased watchman, continually scanning for breaches of safety protocols or signs of exploitation.

Furthermore, AI can help ensure that mining development is aligned with long-term sustainability goals and community interests. By analysing long-term environmental data, such as climate trends, water table levels, and historical land use, AI can help forecast the cumulative impact of mining in a region. For example, it might predict that mining activities plus climate change will severely deplete a region’s water resources in 20 years, informing policymakers to limit water-intensive mining there or require recycling. It can also optimise rehabilitation plans – using ecological data to suggest what plant species to reintroduce on spent mines for best survival chances, or modelling how to contour waste rock piles to minimise erosion. Community well-being could be monitored through AI analysis of health and economic data: if an AI system notes a spike in respiratory illnesses downwind of a quarry, or a drop in agricultural yields near a mine (perhaps via satellite crop health indices), it provides evidence to take action and address these externalises.

In essence, AI can act as an early-warning system and a transparency mechanism for environmental and social safeguards. It helps predict and prevent disasters, ensures continuous oversight rather than sporadic checks, and amplifies the voices of those on the ground by analysing their feedback at scale. For Afghanistan, this means potential mining projects can be pursued with a smarter approach to risk: before a mine is even approved, AI risk models can say “this valley is prone to flash floods that could spread contamination – design the mine accordingly or choose another site.” And once mines are running, digital monitors can constantly whisper in the regulator’s ear about the state of play: “there’s a problem emerging here, fix it now.” Such capabilities will be invaluable to balance resource development with environmental protection and human rights, especially given Afghanistan’s fragile ecosystems and vulnerable communities.

Digital and Institutional Infrastructure Requirements

 

Implementing the above AI and digital solutions will require building up certain digital and institutional infrastructure in Afghanistan’s context. On the digital side, a foundational need is connectivity. While mobile networks reach many parts of Afghanistan, reliable internet connectivity (especially broadband) is still limited – by early 2022 only about 22.9% of the population were internet users, and that figure may have dipped back below 20% under recent conditions ¹⁴. Strengthening digital mining governance will thus depend on expanding connectivity for key stakeholders: government offices (Ministry of Mines headquarters and provincial offices) need consistent internet access, mining sites need at least intermittent connectivity (via 3G/4G or satellite links) to transmit data, and communities need network access to participate in reporting. Investments may be required in basic IT infrastructure like computers, servers, and secure data centres for the ministries. However, given resource constraints, a cost-effective strategy might lean on cloud infrastructure for heavy computing tasks – for instance, using regional data centres (in neighbouring countries or via international cloud providers) to host databases and AI analytics, while local offices connect to these services through improved broadband or even via high-bandwidth satellite terminals. If sanctions or political issues complicate use of US-based clouds, alternative providers (or UNsupported cloud frameworks) might be considered. Additionally, establishing national data repositories – e.g. a centralised geospatial database for all mining-related data (license maps, exploration results, satellite imagery, etc.) – is crucial. This repository should be accessible to various agencies and kept updated, forming the backbone for any AI analysis.

Building human and institutional capacity is equally important. Afghanistan will need to develop its human capital in data science, GIS, and AI to effectively use these technologies. This means training programs and hiring or upskilling staff for key agencies. For example, the Ministry of Mines could create a small “Digital Innovation Unit” staffed with tech-savvy individuals (perhaps young Afghan engineers, or returning diaspora experts) who receive specialised training in using remote sensing software, maintaining databases, and customising AI models for government use. Partnerships with universities (both domestic and abroad) and international organisations can facilitate workshops and courses to train geologists in GIS and machine-learning basics, or train regulators in how to interpret AI outputs. Over time, including data analytics and AI topics in the curricula of Afghan universities (such as Kabul Polytechnic or Kabul University’s geology and IT departments) will produce a pipeline of local talent. In the interim, because domestic expertise is scarce, collaborative projects can bring in outside experts to work alongside Afghans – for instance, UNDP or the World Bank could fund technical advisers to set up the mining cadastre or AI monitoring systems, with a mandate to mentor local counterparts. The ultimate goal is to avoid permanent reliance on foreigners: knowledge transfer must be a core part of any tech integration.

From an institutional standpoint, clear policy and legal frameworks are required to guide the ethical use of AI and digital tools in the Afghan mining sector. Data governance policies should be established to address questions of privacy, security, and ownership of data. For example, if satellite surveillance is used, how will the data be stored and who has access? If communities provide information via mobile surveys, how to protect their identities and ensure the data isn’t misused? Afghanistan will need data protection guidelines – perhaps adapting international best practices to its context – to ensure that sensitive information (whether personal data of miners or confidential geological data) is safeguarded. There should also be cyber security measures in place: as systems digitalize, they could become targets for hacking or manipulation, so investing in basic cyber security training and tools (firewalls, secure authentication, backups) is vital to prevent sabotage or data theft. Additionally, the legal framework around mining should be updated to reflect digital processes: for instance, laws could be amended to recognise electronic records and smart contracts as legally binding, to mandate companies to submit digital data (like GPS coordinates of operations, digital production logs), and to penalise cyber offences like digital tampering with mining data.

Another key institutional requirement is establishing coordination mechanisms among different agencies using these digital tools. Mining impacts and regulation cut across multiple ministries – Mines, Environment, Labor, Finance (for revenues), Interior (for security) – and digital systems can facilitate integrated oversight only if these bodies corporate. Setting up inter-ministerial committees or data-sharing agreements will ensure, for example, that the environmental ministry receives the satellite monitoring alerts relevant to forest or water impacts, or that the anti-corruption commission can access the blockchain ledger of mining transactions if investigating a fraud. In the Afghan context, where institutional capacity is limited, it may even make sense to centralise some digital oversight functions. For instance, a centralised “situation room” that monitors all extractive industries via dashboards (covering production, compliance, and incidents) could be housed in a well-equipped unit that serves all regulators with information.

Ethical use of AI is another policy consideration. There should be guidelines for AI usage that emphasise it as a decision-support tool rather than a decision-maker, to avoid over-reliance on algorithms without human judgement. Given Afghanistan’s governance fragility, one must guard against scenarios where AI tools could be misused – for example, using surveillance drones under the pretext of mining monitoring to actually surveil local populations or political opponents. Accountability and transparency in how AI decisions are made will help here: if an AI model flags a mining company for irregularity, the basis for that flag (the data and logic) should be documented so it can be explained and, if necessary, contested. Publishing open reports about the use of digital tech in the sector (like an annual report on “Digital Mining Governance”) would help build trust and allow public oversight of these tools.

Finally, financing and sustainability of this digital infrastructure must be planned. Donor support can kick-start systems, but the government needs to allocate some budget for maintenance of software/hardware, for continuous internet service, and for retaining trained personnel with competitive salaries. Long-term, the hope is that a well-regulated mining sector will generate more revenue (royalties, taxes) which can help fund these governance tools. In summary, to support AI tools Afghanistan must invest in the nuts and bolts of digital infrastructure (connectivity, devices, cloud or servers), cultivate skilled people to run the systems, and establish policies that ensure the tech is used effectively, ethically, and securely. Without these in place, even the best AI ideas would fail to take root.

Challenges, Risks, and Mitigation Strategies

 

Implementing AI and digital transformation in Afghanistan’s mining sector will not be without significant challenges and risks, given the country’s complex context. It is crucial to anticipate these and devise mitigation strategies:

• Political Instability and Governance Risk: Afghanistan’s political situation remains volatile. Government priorities can shift, and there’s always a risk of conflict or power struggles disrupting initiatives. A new digital monitoring system could become defunct if there is unrest or if future authorities lack interest. Mitigation: Design digital systems to be as resilient and modular as possible. For instance, keep backups of critical data outside conflict zones (or even cloud-based) so that even if a local office is overrun or destroyed, the records survive. Engage a broad base of stakeholders (not just central authorities but also local officials and community groups) in the use of these tools so that demand for them is bottom-up as well. International organisations can be asked to anchor some projects – for example, having neutral oversight by UN or regional bodies might protect an initiative from being abandoned due to internal politics. Also, start with small wins (like a pilot in one province that even skeptical leaders can see benefit from) to build political buy-in gradually.
• Data Security and Sovereignty Concerns: Digital systems and AI rely on data – geological data, company data, personal data from communities. In Afghanistan’s environment, there are fears that sensitive data (like exact locations of valuable minerals) could be stolen or misused by external actors (e.g. foreign intelligence or profiteers), or that digitising data could make it more vulnerable to cyber attacks by insurgent groups or criminals. Mitigation: Implement robust cyber security measures from the outset. Even on a limited budget, basics like encrypting sensitive databases, using secure authentication for system access, and keeping an offline backup can go a long way. If using international cloud services, choose reputable ones and insist on data localisation if possible (e.g. storing backups on local servers under Afghan control). To protect sovereignty, prioritise open-source and Afghan-owned solutions over proprietary foreign systems – this avoids hidden backdoor and ensures Afghans ultimately control the tech. Where foreign technical support is used, include contractual guarantees about data confidentiality and ownership. Regular audits of the digital systems by independent experts can also identify vulnerabilities before they are exploited.
• Lack of Skilled Labor and Institutional Capacity: As noted, Afghanistan currently has a limited pool of experts in AI, IT, and advanced analytics. There’s a risk that expensive systems could be procured but then lie idle for lack of people who can use and maintain them. Brain drain (many educated professionals have left the country) exacerbates this. Mitigation: Emphasise capacity building as part of every tech initiative. Any project implementing an AI tool should include a training component where local staff learn to operate it. Where capacity is extremely low, consider outsourcing with a plan to transition: for instance, initially have an international team run the satellite monitoring and send reports to Afghan authorities, while simultaneously training a local cadre to take over within a few years. Also, leverage whatever talent does exist: perhaps Afghan tech entrepreneurs or NGOs can be contracted to build parts of the system (keeping skills in-country). Creating incentives for diaspora Afghan experts to contribute (even remotely) could help – e.g. a diaspora data scientist might volunteer to develop a machine-learning model for the ministry on weekends if engaged patriotically or via UN programs. To reduce the complexity for users, invest in good user interface design – a tool with a simple dashboard that a non-technical mining inspector can use is better than a complex system only a PhD understands.
• Risk of Misuse or Manipulation of AI Tools: In a fragile governance environment, there is a danger that technology meant for good can be co-opted. For example, data from mining monitoring drones might be used by security agencies for unrelated surveillance on civilians. Or officials might manipulate AI outputs – e.g. altering data inputs to hide a mine’s violations or to falsely accuse a rival – undermining the credibility of the system. Mitigation: Establish clear protocols and oversight for technology use. Define who is allowed to access what data, and log all access and changes (so there is an audit trail if someone tampers with records). Possibly involve third parties (like an independent transparency watchdog or an ombudsman) who have read-only access to the systems to deter manipulation. For instance, if satellite images detect illegal mining, make those images public by default, so no one can quietly ignore or delete that evidence. Training on ethics for those running the systems is also important – they need to understand the importance of impartial data handling. In the case of dual-use equipment like drones, strict rules must delineate that they are for mine oversight only, with punishments if used otherwise. If blockchain is used for transactions, its immutability inherently reduces data tampering risks, which is a reason to incorporate such tamper-evident tech in high-risk areas like financial records.
• Community Acceptance and Socio-cultural Factors: High-tech interventions may face skepticism or resistance on the ground. Local communities might distrust surveillance, fearing it’s a tool of central control, or miners might worry that digital reporting will lead to taxes or interference in their livelihood. It’s also possible that Taliban officials in rural areas might be wary of externally driven technology. Mitigation: Engage stakeholders from the start. Conduct community consultations to explain how these technologies will benefit them – for example, show villagers that environmental monitoring AI will help protect their water from pollution, or that digital transparency can ensure mining revenues are returned to communities rather than lost to corruption. Incorporate local input in system design (e.g. designing mobile apps in local languages, with culturally appropriate interfaces). It’s also wise to produce some early “success stories” that directly help communities – such as using AI analysis to identify a pollution source that, when fixed, improves village health. This creates grassroots support for the tech. For Taliban authorities, frame the narrative around sovereignty and Islamic values – for instance, that these tools will help ensure Afghanistan’s resources benefit the Afghan people (a moral duty) and help prevent theft and exploitation by outsiders. If aligned with their goals and if they can claim ownership of the successes, they are more likely to embrace it.
• Financial Sustainability and Maintenance: A risk is that systems get built with donor support but then cannot be maintained once initial funding ends. Equipment could fall into disrepair and software updates might be neglected, rendering systems obsolete. Mitigation: Plan for sustainability by choosing technologies appropriate to local capacity (for example, using simpler equipment that local technicians can fix, and open-source software that doesn’t require expensive licenses). During project design, include budgeting for at least 5 years of operations and maintenance, and explore revenue streams – e.g. the government might allocate a small percentage of mining royalties specifically to fund the digital oversight unit. Additionally, create documentation and support networks: if staff rotate or leave, make sure manuals and training materials are available for newcomers. Engaging local universities or technical institutes in the project can also provide a pipeline of interns/hires who are familiar with the system and can replace outgoing staff.
• Balancing Technology with Community Needs and National Sovereignty: Finally, there is the overarching challenge of ensuring that the push for high-tech solutions does not sideline the people it is meant to help, nor undermine the country’s control over its resources. If not careful, one could imagine a scenario where foreign companies manage Afghanistan’s “digital mines” remotely, with data flowing out and decisions being made externally – effectively marginalising Afghan workers and authorities. Mitigation: Keep Afghans in the loop of every technological process. Even as AI automates tasks, use it to augment local decision-making, not replace it. For instance, if an AI model identifies a new mineral prospect, the next step should be training Afghan geologists to go verify it, rather than just handing it to a foreign firm. Ensure that any intellectual property (e.g. custom AI models developed for Afghan geology) is owned by Afghan institutions. When partnering with international tech providers, negotiate terms that include source code sharing, local hosting of data, and mandatory training of locals – so that Afghanistan isn’t left dependent on a vendor. Moreover, combine tech roll out with local economic development initiatives: for example, involve community members as paid environmental data collectors or drone operators, so they gain jobs from the new system. By aligning digital modernisation with improvements in local livelihoods and preserving decision authority within Afghan institutions, the reforms will be seen as enhancing national sovereignty and community welfare, rather than a foreign imposition.

The road to a tech-enabled, well-regulated mining sector is fraught with potential pitfalls, but none are insurmountable. With careful planning – focusing on resilience, capacity, ethics, and inclusion – Afghanistan can mitigate these risks. A phased approach that learns and adapts will be key, as early challenges can then inform later stages.

Step-by-Step Roadmap for AI Integration in Afghanistan’s Mining Sector

 

Implementing AI and digital technologies in Afghanistan’s mining sector should be approached in phases – short-term, medium-term, and long-term – with realistic milestones. Below is a step-by-step road map:

Short-Term (Years 1–2): Kick-starting Digital Oversight with Low-Cost Actions

1. Establish a Digital Task Force and Basic Systems: Form a small task force within the Ministry of Mines (and involving the Geological Survey) focused on digitisation. Provide them with necessary training and tools (computers, GIS software). Begin by digitising all existing mining data – create a simple database of all known mining licenses, contracts, and active sites, consolidating information from paper files into an electronic format. Even a basic spreadsheet or a simple GIS map layer of licensed areas is a start if more advanced systems aren’t available. This creates the foundational dataset for any AI analysis.
2. Leverage Readily Available Data and Free Tools: Initiate a satellite monitoring pilot using free imagery. For example, using Google Earth Pro or ESA Sentinel Hub, task the team with monitoring a few known mining hotspots (say, a gold mining district or the lapis mines in Badakhshan) over time. They can manually flag visible changes initially, to get familiar with remote sensing. Simultaneously, reach out to partners like Digital Earth Africa or UNOSAT to obtain historical imagery of Afghanistan. With help from volunteer experts (maybe via groups like Humanitarian OpenStreetMap Team or academic volunteers), attempt a rudimentary machine-learning pilot – e.g. use an open-source Python notebook to classify satellite images of one province into “mining” vs “non-mining” land. This proof-of-concept, even if rough, will demonstrate AI’s potential.
3. Implement a Basic E-Licensing and Reporting System: Develop or adopt a basic online platform where mining companies (or even artisanal mining cooperatives) can register and report production figures. If internet access is an issue for some, allow reports via SMS or offline submission to be uploaded by officials. The key is to start collecting digital data on mining outputs and compliance. An open-source tool or a simple web form could serve this purpose. Announce that all new mining contracts and monthly production must be reported through this system – this improves transparency from day one.
4. Community Engagement via Mobile: Launch a Mining Transparency Hotline – a dedicated SMS/Whats App number where citizens can report illegal mining or grievances. Promote it on local radio. Use a basic text analysis (even keyword filtering) to triage messages. For instance, messages containing words like “child,” “blast,” “river dirty” could be flagged. This establishes an early mechanism for crowdsourced monitoring.
5. Quick Wins on Illegal Mining Enforcement: Using the initial satellite monitoring and community tips, identify 1–2 flagrant cases of illegal mining. Coordinate with local authorities to take visible action (e.g. shutting down an illicit operation or penalising a violator). Publicise these actions. This creates momentum and shows that digital monitoring leads to real-world results, building trust in the new approach. It also provides case studies to learn from (e.g. how accurate was the satellite detection? what evidence was needed on the ground?).
6. Draft a Digital Strategy and Secure Buy-in: In parallel with technical steps, develop a short “Mining Digital Transformation Strategy” document that outlines the vision (much of what is in this report), tailored to Afghanistan’s context. Include clear benefits (increased revenue, reduced corruption, safer mining) to get political buy-in. Have it endorsed by the Mining Ministry leadership (and Taliban officials, if needed) to ensure top-down support. Also, start dialogues with international donors for funding support, using this road map to justify assistance in upcoming phases.

Medium-Term (Years 3–5): Building National Capacity and Integrated Systems

1. Scale Up Infrastructure: By this stage, aim to have improved internet connectivity at least at the central Ministry and key provincial offices (perhaps via satellite broadband where fibre is not available). Invest in a dedicated data centre or cloud service for the mining sector – this will host the central database, applications (like an AI platform for satellite imagery), and backup storage. Ensure redundancy and security features are in place.
2. Integrated Digital Cadastre and AI Monitoring Platform: Develop a more robust Mining Cadastre System – possibly adapting open-source platforms like FlexiCadastre or others used in Africa. This system should map all mining licenses on an interactive map of Afghanistan, and store all related data (ownership, duration, payments, etc.). Layer this with an AI monitoring dashboard: for example, a system that automatically imports new satellite imagery (monthly) and uses a refined machine-learning model to identify changes (new pits, expanding mine area) in each licensed area as well as detect unlicensed activity in known mineral corridors. The dashboard could show a “risk score” or alert for each site. To achieve this, likely partner with international experts (e.g. satellite analytics firms or the academic community) who can help train models using Afghan data. The model accuracy will improve over time as more validation is done.
3. Capacity Building and Institutionalisation: By year 3, have identified bright young professionals (perhaps 5–10 people) who will be the core of the Digital Mining Monitoring Unit. Send them for specialised training – for instance, a 3-month intensive course on GIS/remote sensing (maybe to a regional centre in India or UAE), and an attachment with a foreign geological survey to see AI applications. In return, these staff become trainers for others. Also, introduce basic AI and data analysis modules into the curriculum of Afghan Geological Survey training programs or through workshops for ministry staff. Aim to cultivate a local community of practice – even a small “Afghan Mining Tech” working group or conference that meets periodically to share knowledge.
4. Enhance Legal and Policy Frameworks: Work with legal advisers to update mining regulations to mandate digital processes. For example, require all medium-large mining companies to install GPS trackers on mineral transport trucks and feed that data to the ministry (allowing AI to track movements and detect deviations). Require companies to submit quarterly environmental monitoring data digitally, which can be cross-checked. Develop guidelines for drone use in mine inspections (who can fly, privacy considerations). Also finalise and enact policies on data sharing and protection – e.g. a clause that all geological data collected by companies must be reported and becomes part of the national database after a certain period. These legal tweaks ensure that as tech comes in, it’s supported by law and not optional.
5. Transparency and Blockchain Pilots: In these years, implement a pilot blockchain project for one supply chain. A good candidate might be a high-value, small-volume commodity like gemstones or gold. For instance, work with local lapis traders and international buyers to put a batch of lapis lazuli on a blockchain-based traceability system. Each hand off from mine to trader to exporter is recorded, and perhaps even verified by an NGO or third-party. This pilot can be done on a small scale to iron out challenges. If successful, it could be expanded or replicated for other minerals (like lithium, if extraction starts, to assure international markets of its origin). Additionally, continue improving public transparency: maintain an up-to-date public portal where all mining contracts, licenses, and even the outputs of the monitoring system (sanitised as needed) are visible to citizens. For example, a map showing all active mines with status (legal/illegal/in review) can be published online.
6. Environmental and Social AI Use-Cases: By year 4–5, introduce more specialised AI applications for safety and environment. This could include deploying a few sensor stations at large mine sites (perhaps at a coal mine and a marble quarry as demos) which monitor particulate dust or water quality and send data to the central system; then use AI to analyse trends from these sensors. Also possibly equip inspectors or local partners with drones and train them to use AI-powered image analysis for environmental checks (like counting how many trees were cut at a site versus the replanting done). On labor, scale up the mobile survey program: partner with organisations like Ulula or ILO to roll out the mobile labor rights reporting nationwide in mining areas, and have the AI system produce quarterly “risk reports” highlighting regions or companies with frequent labor issues. Ensure these reports are reviewed by ministry leadership and lead to actions (inspections or fines) to close the feedback loop.
7. Mid-Term Review and Adjustments: At the end of year 5, conduct a comprehensive review of progress. Assess which AI applications are working well and which are lagging. Gather feedback from users – the analysts, the provincial officers, even companies and community members. For example, maybe the satellite AI flagged many false positives in mountainous shadows; then plan to improve the model. Or maybe companies complain the reporting system is cumbersome; iterate to make it more user-friendly. Use this evaluation to refine the long-term plan.

Long-Term (Year 6 and beyond): Vision for a Digitally Regulated, Sustainable Mining Industry
In the long run (5–10+ years), Afghanistan should aim for a fully digitised and AI-augmented mining governance ecosystem. The vision includes:

• Integrated National Resource Platform: A central platform that integrates all data – geological maps, license info, production data, export stats, environmental metrics – and uses AI to provide a real-time overview of the sector. Think of a command centre where a map of Afghanistan shows all mining locations as live icons: green if all’s well, yellow if some indicators are out of range, red if a serious violation or incident is detected. Decision-makers can click any site to drill into the AI-analysed details (e.g. “satellite shows expansion beyond permit by 30%; drone image shows possible child labor; water pH downriver is 5.2 indicating possible acid runoff” etc.). This platform would be used in daily management meetings to prioritise enforcement and support decisions on new licenses (AI could even simulate the outcomes of granting a new license in a certain area, given environmental and social data).
• Routine Use of Advanced Technologies: By this stage, AI and automation would be embedded in operations. For example, licensing may use AI-assisted evaluation (with algorithms quickly checking applications against all database records for conflicts or past company performance). Autonomous or semi-autonomous equipment might be introduced at major mine sites, improving safety – e.g. self-driving haul trucks in a large open-pit mine or automated drilling rigs – reducing the risks to Afghan workers and improving efficiency. Afghan engineers and technicians would be running these systems after training. IoT sensors all over mines (on vehicles, in shafts, in tailings dams) would continuously feed data on vibrations, gas levels, structural stability, which AI algorithms analyse to warn of any danger (preventing accidents like tunnel collapses or methane explosions in coal mines).
• Sustainable and Inclusive Practices Ingrained: Long-term planning would use AI models to ensure sustainability. Before any new big mine is approved, an AI-driven environmental impact simulation is standard, and only if the model shows manageable impact with mitigation (and perhaps even climate change scenarios) is it allowed. Renewable energy and mining would be paired – for instance, solar farms powering mines, with AI optimising energy usage. Communities would be fully integrated via digital tools: local people might have community monitoring apps that feed directly into the oversight system, and they could receive transparent info on how much revenue was generated and allocated to their district (possibly via a blockchain-based public finance tracker for mining royalties). This level of transparency and inclusion helps prevent the “resource curse” by making sure locals benefit and know what is happening.
• Institutionalised Knowledge and Innovation: By year 10 or beyond, Afghanistan could become a regional leader in certain aspects of digital mining governance. Its Geological Survey and universities might be actively conducting their own AI research on mineral prospectivity or environmental management, creating home-grown solutions. Perhaps an Afghan-developed AI tool for mapping artisanal mining or a Dari/Pashto language AI for processing community reports is exported to other countries facing similar issues. The government would have dedicated budget lines for maintaining and upgrading digital infrastructure, and a steady cadre of trained professionals. Policies would also be updated continuously to keep pace with tech (for example, regulations on mine data sharing or periodic algorithm audits to ensure AI fairness).
• Resilience and Sovereignty: In the long-term vision, Afghanistan’s use of AI in mining underpins its economic sovereignty. The country would have full knowledge of what resources it has (thanks to AI analysis of exploration data) and control over their development. The digital systems would reduce theft and illicit flows, meaning more resource revenue enters state coffers and local budgets for development. Because of high transparency and better enforcement, Afghanistan could negotiate better deals with foreign investors (who know they must operate cleanly or the AI will catch irregularities). Also, robust digital records could support Afghanistan in international arbitration or disputes – for instance, if a company under-reports, the government has the data trail to claim what is owed.

The journey to this long-term state needs continuous adaptation. As AI technology evolves (e.g. next-generation Earth observation or new data science techniques), Afghanistan’s systems should be updated. The governance model should also adapt – perhaps eventually spinning off an independent “Minerals Regulatory Authority” that operates these digital oversight systems at arm’s length from political influence, ensuring impartiality. Community trust built over years of fair, data-driven management will be crucial to keeping social license for mining. In essence, the long-term goal is a virtuous cycle: better data and AI lead to better governance, which leads to more revenues and stability, which then allows further investment in technology and human capacity, and that again improves governance.

Each step of this road map builds on previous ones: starting small and simple, proving value, then scaling up complexity and reach. While ambitious, this phased approach acknowledges Afghanistan’s current limitations and gradually transforms them. If successfully executed, within a decade Afghanistan’s mining sector could leapfrog into a new era – one where modern technology helps ensure that the country’s abundant minerals are extracted responsibly, transparently, and efficiently for the benefit of its people.

 

References

 

1. Afghan Witness (Centre for Information Resilience). Afghanistan’s mining sector under the Taliban (Investigation Report, June 2024). – Describes the post-2021 mining landscape, including 205 new contracts issued by the Taliban and issues of corruption and ownership info-res.org.
2. Reuters (T. Daly & S. Singh). What are Afghanistan’s untapped minerals and resources? (Aug 19, 2021). – Summarizes Afghanistan’s key mineral deposits (copper, iron, gold, lithium, rare earths, gemstones, etc.) and their estimated quantities and values reuters.com.
3. Blumenthal, L. et al, Brookings Institution. Chinese investment in Afghanistan’s lithium sector: A long shot in the short term (Aug 3, 2022). – Analyses Afghanistan’s limited capacity to develop minerals (like lithium) without outside help, noting infrastructure and technical constraints, and highlights that Taliban-era mining is mostly artisanal, low-tech extraction brookings.edu.

4. U.S. Geological Survey (USGS). New Maps of Afghanistan Provide “Fingerprint” of Natural Resources (Press Release, March 10, 2014). – Announces the release of 60 hyperspectral mineral maps covering ~70% of Afghanistan, the first country mapped almost entirely with this high-tech method, identifying numerous mineralized zones usgs.gov.

5. Pulitzer Center & Earthrise Media. New AI Platform Monitors Mining in the Amazon Rainforest (2023). – Describes the Amazon MiningWatch platform that uses an AI algorithm to analyze satellite images and automatically detect gold mining activity over millions of hectares in the Amazon, demonstrating remote illegal mining detection pulitzercenter.org.

6. Amazon Web Services Public Sector Blog (A. Privette). How African leaders use open data to fight deforestation and illegal mining (Dec 1, 2021). – Details the Digital Earth Africa initiative which provides free Earth observation data; notes that it helped Ghana identify and address illegal mining using satellite imagery and cloud services aws.amazon.com.
7. IBM News. Blockchain for the mining industry: Ethical cobalt production (2019). – Outlines a pilot project using IBM’s blockchain platform to trace cobalt from a mine in DRC through refining to an end-manufacturer (Ford), aiming to ensure responsible sourcing and providing end-to-end supply chain transparency ibm.com.
8. Ulula (Press Release). Ulula receives ILO award supporting innovation to end child labor, forced labor and human trafficking (Nov 18, 2021). – Introduces a mobile-based AI “tracker” platform for labor rights, which collects survey data via SMS/voice in the DRC and uses AI to predict and identify risks of child labor and forced labor in mining and other sectors ulula.com.
9. Fatigue Science. Top 7 Ways AI Is Enhancing Safety in Mining Operations (Blog, 2023). – Explains various applications of AI in mining safety, including predictive hazard identification; notes that AI can analyze geological and weather data to forecast incidents like landslides, enabling proactive measures to protect workers and the environment fatiguescience.com.
10. Emerj (J. Walker). AI in Mining – Mineral Exploration, Autonomous Drills, and More (Oct 30, 2017). – Reviews how the global mining industry is adopting AI, with examples like Goldspot Discoveries using machine learning to find new gold deposits, and companies deploying autonomous haul trucks and AI-driven safety monitoring (e.g., fatigue detection caps) emerj.com mine.nridigital.com.
11. Special Inspector General for Afghanistan Reconstruction (SIGAR). Quarterly Report to US Congress (April 2023) – Section on Extractives. – Notes that from 2004–2021, the U.S. invested ~$962.6 million in Afghanistan’s mining sector (surveys, training, institutional support) with minimal success in building a self-sustaining, well-regulated industry info-res.org.
12. Afghan Ministry of Mines and Petroleum (2019). Afghan Mining Roadmap and Survey Report. – Internal report cited by Reuters giving resource estimates: e.g. ~30 million tonnes of copper (discovered), ~28.5 million tonnes more inferred; 2.2 billion tonnes of iron ore; 1.4 million tonnes of rare-earth elements; also discusses hydrocarbons and gemstones, and highlights that most gemstones are illegally exported reuters.com.
13. Peace and Conflict Resolution Evidence Platform peacerep.org
14. Global Satellite Services TS2 Space ts2.tech
15. Global Mining Review globalminingreview.com

16. Feature Map of Minerals used in this report is extracted from the following file: https://pubs.usgs.gov/ds/624/images/Fig01.pdf

 

 

 

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