Introducing the AI Ethics Tool Landscape

Many companies that employ AI by now recognize the need for what the European Commission calls an “ecosystem of trust” 1. This has resulted in a large amount of (ethical) principle statements for the usage of AI. These statements are often to a large degree inspired by pre-existing guidelines and principles, such as the European Commission’s Ethics Guidelines for Trustworthy AI 2 or the SAP guiding principles for AI 3. However, there are doubts about the effectiveness of these guidelines. A main challenge for the coming years is to effectively operationalise AI policy. In this post I introduce an open source project that hopefully makes a step in the right direction by listing available tools to support the ethical development of AI.

The tools are described and organized in the AI Ethics Tooling Landscape . The website itself and the corresponding README explains how the website works and how to contribute to it.

Where guidelines fall short

AlgorithmWatch initiated an AI Ethics Guidelines Global Inventory 4 in which more than 160 AI ethics guidelines have been collected. The organizers of this initiative noted that the overwhelming majority of guidelines contained general promises, for example to not used biased data, but no concrete “recommendations or examples of how to operationalise the principles” 5. Evaluating the guidelines in this repository a year later, AlgorithmWatch states that often “it is a matter of weaving together principles without a clear view on how they are to be applied in practice. Thus, many guidelines are not suitable as a tool against potential harmful uses of AI-based technologies and will probably fail in their attempt to prevent damage. The question arises whether guidelines that can neither be applied nor enforced are not more harmful than having no ethical guidelines at all. Ethics guidelines should be more than a PR tool for companies and governments” 6. Because most ethical guidelines do not indicate “an oversight or enforcement mechanism” 5 they risk being rather toothless “paper tigers.” Ethics does by nature not include any mechanisms for its own enforcement and the active promotion of ethics guidelines is even seen by some as an attempt to preemptively stifle the development of more rigid AI legislation that does enforce compliance 7. This suspicion that companies use ethics guidelines as a PR tool can lead to accusations of ethics washing 8.

A call for operationalisation

This shows that despite a proliferation of principle statements — the “what” of ethical AI — there is not enough focus on the “how” of ethical AI. Papers that review the operationalisation of AI principles point out that currently these principles do “not yet bring about actual change in the design of algorithmic systems” 9. In a similar vein, Hagendorff points out that “it is noteworthy that almost all guidelines suggest that technical solutions exist for many of the problems described. Nevertheless, there are only two guidelines which contain genuinely technical explanations at all — albeit only very sparsely” 7. Equally stern, Canca concludes that “the multiplication of these mostly vaguely formulated principles has not proven to be helpful in guiding practice. Only by operationalizing AI principles for ethical practice can we help computer scientists, developers, and designers to spot and think through ethical issues and recognize when a complex ethical issue requires in-depth expert analysis” 10.

These citations from review papers show that there are serious doubts, to put it mildly, about the effectiveness of guidelines. These guidelines are great public statements, but how do we make sure they actually impact the work being done in professional AI communities? A main challenge for AI ethics and ethical AI in the coming years is therefore “to build tangible bridges between abstract values and technical implementations, as long as these bridges can be reasonably constructed” 7.

Bridging the gap

This call for concretisation and operationalisation of AI principles does not mean, however, that AI principles can always and straightforwardly be technically implemented in a tool or computer program. This tension between abstract principles and concrete tools requires thoughtful navigation. From the perspective of principles, there is a strong call to make them as concrete as possible so that they can be used by the professionals actually creating AI applications. In this context, Hagendorff calls for a “microethics” 7 that engages with technical details, such as the way algorithms are designed, or how data is used throughout a machine learning pipeline. From the perspective of existing tools and techniques, it should instead be emphasized that they are not “plug-and-play” solutions to make an AI application ethical. Instead, appropriate use of these tools requires an ethical sensitivity, with attention to the context in which an AI technique is embedded and used. For example, if you use a fairness tool, which definition of fairness is used and is this definition appropriate given the type of application you are developing?

The AI Ethics Tool Landscape

To these ends, I did an explorative study of the available tools and techniques for ethical AI. The target audience that would use these tools are developers, so I wanted to tailor the format in which I presented my insights to developers. The majority of the tools are technical, but the value “accountability” includes non-technical tools, that nevertheless meet the criterion that they provide hands-on guidance for developers to engage with ethical AI. Instead of writing a large document on these tools — which no developer would ever read — I decided to program a website from scratch in the style of a wiki.

The website is called the AI Ethics Tooling Landscape and is set up as an open source project. The website and corresponding README explains how the website works and how to contribute to it. Specifically, because the project content is completely based on simple text files that are automatically parsed, little to no technical know-how is required to contribute content.

Each tool is placed in a conceptual taxonomy, which I based on 1) existing typologies and taxonomies for categorizing (machine learning) tools 911, 2) insights from my study of e.g. explainability and fairness, 3) things that are very practical to know for developers, such as the programming language of the tool or which frameworks are used. For example, Morley et al. used a matrix with ethical values on the x-axis and the development stage in which a technique applies on the y-axis. Similarly, the wiki categorizes tools by the value they support (accountability, explainability, fairness, privacy, security) and the stage in which they are useful (design phase, preprocessing, in-processing, post-processing). I fine-tuned the conceptual taxonomy incrementally based on feedback from developers. Morley et al. use more stages, but this results in an extremely sparse matrix. Based on feedback that the stages were indeed a bit too complicated, I came to the current broader stage categories, which are also used in the fairlearn tool.

However, due to the digital design of the website, I was not limited to a 2D matrix. Tools are also categorized on the following properties: whether they are model-agnostic or -specific; which type of data the tool handles; which type of explanation is supported (if applicable); which type of fairness is supported (if applicable); which programming framework is compatible (e.g. PyTorch); which programming languages are supported; and which machine learning tasks are relevant.

An interesting observation is that the tooling landscape is not uniformly distributed. Significantly more technical tools are available for the values explainability and fairness than for privacy and security, which reflects that the research field on topics like differential privacy and adversarial AI are relatively young.

At the time of writing, the website project contains 41 custom-made tool entries. Each of these entries contains metadata to categorize the tool, as well as my description of the tool with relevant information. All values, stages, explanation types, and fairness types have their own guiding descriptions as well.

  1. European Commission. White Paper On Artificial Intelligence - A European approach to excellence and trust. Tech. rep. Brussels: European Commission, 2020, p. 27. ↩︎

  2. High-Level Expert Group on Artificial Intelligence. The European Commission’s high-level expert group on Artificial Intelligence: Ethics guidelines for trustworthy AI. Tech. rep. European Commission, Apr. 8, 2019, pp. 1-39. ↩︎

  3. Corinna Machmeier. SAP’s Guiding Principles for Artificial Intelligence. Sept. 18, 2018. url: (visited on 08/04/2021). ↩︎

  4. AlgorithmWatch. AI Ethics Guidelines Global Inventory. url: (visited on 03/16/2021). ↩︎

  5. AlgorithmWatch. Launch of our ‘AI Ethics Guidelines Global Inventory’. Apr. 9, 2019. url: (visited on 08/04/2021). ↩︎

  6. AlgorithmWatch. In the realm of paper tigers - exploring the failings of AI ethics guidelines. Apr. 28, 2020. url: (visited on 08/04/2021) ↩︎

  7. Thilo Hagendorff. “The Ethics of AI Ethics: An Evaluation of Guidelines”. In: Minds and Machines 30.1 (2020), pp. 99-120. doi: 10.1007/s11023-020-09517-8. ↩︎

  8. Elettra Bietti. “From Ethics Washing to Ethics Bashing: A View on Tech Ethics from within Moral Philosophy”. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. FAT* ‘20. Barcelona, Spain: Association for Computing Machinery, Jan. 2020, pp. 210-219. doi: 10.1145/3351095.3372860. ↩︎

  9. Jessica Morley et al. “From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices?. In: Science and Engineering Ethics 26.4 (2020), pp. 2141- 2168. doi: 10.1007/s11948-019-00165-5. ↩︎

  10. Cansu Canca. “Operationalizing AI Ethics Principles”. In: Commun. ACM 63.12 (Nov. 2020), pp. 18-21. doi: 10.1145/3430368. ↩︎

  11. Alejandro Barredo Arrieta et al. “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI”. In: Information Fusion 58 (2020), pp. 82-115. doi: https://doi. org/10.1016/j.inffus.2019.12.012. ↩︎

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