Artificial Intelligence and Human Rights Due Diligence – Part 1. Integrating AI into the HRDD process - By Samuel Brobby

Editor's note: Samuel Brobby graduated from Maastricht University's Globalisation and Law LLM specialising in Human Rights in September 2020. A special interest in HRDD carries his research through various topics such as: the intersection between AI and HRDD, the French Devoir de Vigilance or mHRDD at the EU level. Since April 2021 he has joined the Asser Institute as a research intern for the Doing Business Right project.

The recent surge in developments and debate surrounding Artificial Intelligence (AI) have been business centric, naturally so. The conversation has long been centred on the possible gains “digitally conscious” companies can recoup from their sizeable investments in the various forms this technology can take. The ink continues to flow as numerous articles are released daily; debating between the ultimate power of artificial intelligence (and topical subsets like machine learning) on the one hand, versus the comparatively more philistinish views regarding what these technologies can offer on the other. Our objective here is not to pick a side on the AI debate. Rather, we would like to explore the Business & Human Rights implications of the development of AI and, in particular its intersection with the human rights due diligence (HRDD) processes enshrined in the UN Guiding Principles on Business and Human Rights and subsequent declinations. How compatible is AI with HRDD obligations? Where does AI fit into the HRDD process? Can AI be used as a tool to further HRDD obligations? Can the HRDD process, in return, have an effect on the elaboration and progress of AI and its use in transnational business? And, to which extent will the roll out of AI be affected by HRDD obligations? These are all questions we hope to tackle in this blog.

In short, it seems two distinct shifts are occurring, rather opportunely, in close time frames. The impending mass adoption of AI in transnational business will have strong consequences for the state of Human Rights. This adoption is not only substantiated by an uptick of AI in business, but also in policy documents produced or endorsed by leading institutions such as the ILO or the OECD for instance. Inversely, we must consider that HRDD obligations elaborated by the BHR community will also have strong implications for the development and roll out of AI. These two transformations will interact increasingly as their positions are consolidated. It is these interactions that we wish to analyse in the two parts of this article. Namely, the emergence of Artificial intelligence as a tool to shape and further HRDD obligations (1) and the emergence of HRDD as a process to shape the development of AI (2).

AI as a tool to shape and further the HRDD process

We will begin with an analysis of how artificial intelligence can support the HRDD process, taking a special look at how certain AI algorithms can be harnessed to conduct HRDD. For this analysis AI can be generally understood as defined by the European Commission’s recent AI regulation proposal as “software that is developed with one or more of the techniques and approaches (…) and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with”. HRDD is understood as outlined in the OECD due diligence guidance for responsible business conduct, the OHCHR Interpretative guide on the corporate responsibility to protect HR’s and the UNGP’s. As such, this article will follow along 4 major components of the HRDD process: Identifying potential risks, identifying adequate actions, tracking implementation/results and grievance mechanisms. The aim is to ascertain whether, and to which extent, AI is a tool that can be integrated into the HRDD process.

AI’s ability to sort through, process, analyse and make conclusions from data fits well with what is increasingly being asked of businesses in the framework of HRDD. Identifying, preventing and tracking are all terms synonymous with both Artificial intelligence and HRDD. What’s more, the requirements to cease, mitigate and/or remediate adverse HR impacts of businesses could as well benefit from the abilities of certain AI algorithms to support efficient human decision-making. In short, it seems that theoretically there is not an aspect of the HRDD process that could not benefit from the potential input of AI. The first section of this article will take a deeper dive into this consideration to show the compatibility between AI and HRDD.

In researching this article, particular care was taken to ensure that a number of professionals in this field were consulted with the goal of espousing theory with the reality of the current situation. The overwhelming response is the following: The idea of implementing AI into the HRDD process is, for the moment, very far from being put in place in practice. In truth there is no guarantee that corporations will devote the resources required to meaningfully integrate AI into their HRDD process, it would be optimistic to expect them do it purely out of bona fides intentions for a better world. However, this distance between the possibilities of AI in HRDD for the future and the current state of play should not be an obstacle to discussion. On the contrary, these discussions and decisions that are taken at the intersection between HRDD and AI will have huge implications on the future state of Human Rights.

AI and the identification and assessment of actual or potential adverse human rights impacts 

It seems that the most natural application of AI with regards to the due diligence process falls within the direct use of AI’s predictive capabilities to identify adverse impacts on HR. The predictive capabilities of certain AI algorithms fall hand in hand with the essence of this aspect of HRDD. AI has already found applications in risk evaluation in a whole host of different sectors. With sufficient quality data, AI could allow for potential Human Rights issues to be identified long before they take place with an analogous implementation of technology, that already exists, into HRDD.

AI’s predictive capabilities exceed human predictive capabilities and naturally so given the bandwidth at which AI can potentially operate, allowing it to sort through excessively large amounts of data at lightning-fast speeds. AI can be used to render businesses more profitable by increasing their efficiency using predictive algorithms, but corporations could also render their businesses more socially and environmentally viable by strengthening their HRDD process with AI tools. To punctuate this, I would like to use 2 examples: Big data processing through the use of machine learning and computer vision to identify and prevent issues long before their effects are apparent.

Big data processing possibilities continue to grow as corporations move increasingly towards integrating digital means within their operations. The increased use of computing, connected items (such as Internet of things) and other digitally powered items contributes to the exponential growth of data. Such data finds its sources in these increasingly connected businesses, as it does from the different stakeholders which interact with these groups. In-house data is supplemented by the flow of external data to form a deep pool of valuable information. HRDD operators cannot get familiar, let alone analyse, this inconceivably huge and ever-growing mass of data. Yet, someone or something with access to a shared pool of this data has access to a mine of knowledge of the very workings and impacts of a corporation and its value chain, down to the smallest connected parts. Artificial neural networks (ANN) or other subsets of machine learning can be deployed to find patterns and interrelations in the data that were previously not visible. The applications regarding risk mapping or assessment of an enterprise’s involvement in actual or potential adverse human rights impact (2.3 OECD guidelines) are clear in this respect. These could involve ideas like being able to predict optimal maintenance times to avoid breakdowns of machinery and environmental spills. It could also include being able to compute accurately and predict the environmental effects of operations with a view for potential vulnerable stakeholders (like employees or proximate third parties for instance). Finally, ANN’s could likely reveal deeper interconnections pointing to adverse human rights impacts arising from business related activities that human eyes would not have even considered. There are a wide range of possibilities through which AI can be applied to assist risk assessment in the HRDD process.

Computer vision  is another subset of machine learning which entails converting digital imagery into models within which trained AI agents are able to analyse, identify and interpret what they “see”. This could benefit environmental due diligence for instance. This could be done by setting camera traps able to periodically review flora, fauna, soil or water samples in a sector relating to business activities of a corporation or its subsidiaries to get clear indications of environmental impact of business activities. As such, these perception-based agents can be of use to human decision makers in the HRDD process. Data-sets are already being created and refined in the sector of protection of endangered species. The use of such technology to monitor certain indicator species or environments closely related to business activities should be considered a potential additional option for businesses in their impact assessments. AI could provide a helping hand for human decision makers in this respect by illuminating previously obscure information and signalling with more precision the areas upon which corporations need to act in their HRDD process.

AI and the identification of adequate actions to prevent adverse human rights impacts

After identifying potential adverse human rights impacts arising from business related activities of a corporation directly, or through entities in its value chain, then comes the need to identify adequate actions to prevent or mitigate their materialization. In the case where identifying adequate action and integrating those findings into a HRDD plan involves the activities of a corporation (or closely related subsidiary) directly, the situation is relatively simplified. Here, predictive AI, with the help of heuristics, can be deployed to enact a plan that has a reasonable chance of tackling the risks, since it depends upon entities which are under the direct control of the parent company. Complications arise in the case of indirect business relationships down the supply chain for which the chain of command is not direct. Herein lies an interesting possibility to espouse AI, HRDD and the notion of leverage to potentially apply pressure throughout the value chain.

UNGP 19 and its subsequent commentary elucidate the expectation for businesses to use their leverage to mitigate HR risks in their global value chains. Here, AI may have an important role to play in increasing the effectiveness of corporations. The possibility of deploying AI to relink the value chain and reassign responsibility to decision making entities within it, can exist, supported by the notion of “leverage” as it is understood by the UNGP’s. Much is made of the inability (or costly burden) to reconnect increasingly outsourced, runaway supply chain constellations. For instance, Intel’s 2020 CSR report counts 10000+ tier-1 suppliers over 89 different countries. This number grows exponentially as you descend down the tiers. It would be difficult to expect Intel to have a close understanding with each supplier to the point where they could exercise the leverage required to fulfil the expectations of UNGP 19. Here too, AI’s potential exists for corporate groups sitting atop of their long value chains. As goods and services are manufactured and delivered, stopping by each cog in the value chain, so too does data. It is created, added, shared and transferred from the smallest connected parts of supply chains right through to delivery for the end consumer. The possibility of returning upstream to identify links in the chain should therefore exist.

Development of Fuzzy logic algorithms could be interesting in this respect. This subset of symbolic AI may offer a technological platform for principles of prioritisation and proportionality, associated to leverage and HRDD, to take form. Fuzzy logic can be understood in opposition to “crisp” computer logic which confers a definitive “yes or no” answer to a given problem. Fuzzy logic can allow for the determination of nuance and degrees of truth within a given situation. This could be of use in the hugely complex global value chains characterised by its huge number of moving parts. Research on the use of such algorithms in supply chain management does exist, however to my knowledge, not focusing on applying it to HRDD. Here, fuzzy logic could play a role in determining which entities to put under pressure (or leverage) and which impacts to prioritise to ensure the maximum efficiency of an action plan. Fuzzy logic algorithms could be an interesting future HRDD tool that could help human decision makers in identifying, with precision, the pressure points that need to be acted upon to ensure the adequacy of their responses to human rights risks.

AI and tracking of the effectiveness of actions taken to prevent adverse human rights impacts

The potential use of AI in the HRDD process is also visible at the stage of tracking implementation and results. AI powered analytics could in theory allow for a more accurate assessment of the effectiveness of the responses emanating from the prior steps of the HRDD process. Providing human decision makers with a broader palette of insight from which conclusions can be drawn. What’s more, given that the HRDD process is not static, it relies on constant reassessment to ensure that the processes in place are actually effective. AI agents analysing the steady flow of data will be able to track implementation and ensure the compilation of accurate results of HRDD processes undertaken by corporations. Analysis of such implementation in turn feeds back into elaborating the best choice of actions, potentially enabling a more effective HRDD process under the control of human decision makers.

AI and grievance mechanisms

HRDD for the most part is a process that relies on ex-ante assessments of potential risks in a bid to avoid them from materialising. However, the HRDD process does not stop at the assessment of risk and the requirement of acting upon it. HRDD carries through to the requirement of enabling access to remedy in cases where a risk materializes. Here, AI may have its applications too, especially if you consider the potential incentive of providing effective internal grievance mechanisms over the increasing possibility of certain civil liability frameworks that are being considered. What role can AI play in the establishment of grievance mechanisms aimed at enabling access to remedy and at tracking the effectiveness of a company’s HRDD process?

The use of anonymous complaint platforms and AI powered chat-boxes can be an interesting starting point to enable internal discussion between stakeholders (like employees) and the corporation for which they are involved with. Empowering those internal voices might provide an opportunity for corporations at the top of their supply chains to gain additional insight on their global Human Right’s impact by identifying potential clusters. Additionally, it may enable certain affected stakeholders to open a conversation in a view of achieving redress. Of course, internal whistleblowing is by no means a new creation, but it could benefit from the analysis and treatment provided for by certain AI agents. The use of AI to establish a failure in HRDD obligations by identifying that a corporation knew (or ought to have known), and mitigated a given risk-turned-to-damage, could be an interesting avenue to explore. The implementation of AI in the prior steps leading up to a failure of a HRDD plan could naturally increase the efficiency regarding the provision of remedy.


It is perhaps too early to definitively state the place AI will occupy in the mHRDD process; luckily, this is not the purpose of this article. This section aimed to present a preview of the possibilities that AI can provide to further this process. To that end, the potential integration of AI into the HRDD process seems plausible, especially in terms of identifying potential risks and action plans for maximum effect. The reason we can say this with confidence is because we can see AI being implemented for risk evaluation in many sectors across the board. On the flip side of the coin, integrating AI into HRDD does come with potential challenges. For instance, as mentioned above, the use of data required for a number of these processes would likely be down to consent or contractual obligation from links in the supply chain. Inversely, widescale acceptation would reinforce the asymmetrical nature of relations between suppliers and corporate groups sitting atop of their chains by allowing them to withhold and process a massive amount of data “in the name of HRDD”. In that regard, safeguards must be considered in order to ensure that potential residual effects (monopolisation of data or AI in support of greenwashing to name a couple) arising from a willingness to improve HRDD do not disproportionally offset the situation in practice.

What may be required is an incentive for corporate groups to integrate AI into HRDD. In this regard, an economy of scale argument could be interesting to consider. If a company develops and places a centralised AIHRDD technology on the market at a competitive rate (by offering to cut down the number of human employees in this sector for instance) then it would be very intriguing to see how fast it would spread. However, at this point in time little movement has been identified regarding the development of AI to be deployed specifically for the HRDD process. Be it from my own research, interviews with professionals working in and around HRDD, or interviews with academics taking an interest in this field, I have been unable to identify traces of the existence of such an initiative. As such, another potential effect of this publication could be to incite and invite thought and cooperation around such a project. Early as we may be in the adoption of AI, the nature of the HRDD process offers an opportunity for AI integration.

With that being said, AI in itself is no panacea, it is not a remedy to fix all of transnational business’s adverse impact on human rights. Whilst it may potentially offer some new possibilities in terms of HRDD, AI remains a type of technology whose effects are dependent on how we implement and use it. Whether or not this will improve the HRDD process without significant draw backs remains to be seen.

The first part of this contribution has attempted to show the potential applications of AI as a tool to further the HRDD process. The following part of this article will focus on the reverse trend. What are the risks associated with the widespread implementation of AI in society? How will HRDD obligations affect the development and roll out of AI? To which extent will the responsibility of AI developers regulate the agents they create? And what role will the BHR community have in ensuring that the practical applications of technological progress does not come at the detriment of Human Rights?

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