Data 2020: AI and algorithmic accountability
AI and algorithmic accountability is one of the areas we’ve identified in our Data 2020 landscape review to help organisations understand hot topics in the world of data in 2020 – from digital competition to data rights
AI and machine learning algorithms are increasingly being used to make decisions – including decisions about us. But how are these decisions made? How can algorithms be interrogated or understood, and how do we ensure that unfair bias isn’t being built in – even unintentionally? More fundamentally, should some decisions be automated at all?
The UN Special Rapporteur on extreme poverty and human rights has highlighted the impact of AI and digital technologies on people’s lives. They are being used in crucial decisions such as eligibility assessment, fraud prevention and detection, and risk scoring and needs classification.
AI Now has detailed the range of algorithmic systems used by the public sector. The UK’s Centre for Data Ethics and Innovation is examining bias in algorithmic decision-making in financial services, crime and justice, recruitment and public services, and is creating recommendations about how any potential harms can be identified and minimised.
Transparency and an organisation’s ability to explain the AI algorithms it uses builds trust. It also enables teams to monitor how decisions are made, and if necessary address failings, bias or problems in the system. Understanding and auditing automated decision-making is critical for society – not just in terms of ensuring decisions are accurate, non-discriminatory and fair, but to ensure people can maintain their autonomy.
Upturn and Omidyar Network have described how to design systems for accountability. The Alan Turing Institute and the UK’s Information Commissioner’s Office have also developed guidance for improving the explainability of AI.
- Regulating accountability and transparency mechanisms for private sector use of AI
- Engaging citizens around public service automation, including the use of facial recognition and biometrics
- Integrating AI into human decision-making
- Improving the explainability of algorithms created by machine learning
- Monitoring and responding to the impacts of algorithmic decision-making
- Upturn and Omidyar Network: Public Scrutiny of Automated Decisions: Early Lessons and Emerging Methods
- AI Now: Algorithmic Accountability Policy Toolkit
- Information Commissioner’s Office and The Alan Turing Institute: Consultation on explaining AI decisions guidance
- Nesta: Artificial intelligence – maximising the public benefit
- Centre for Data Ethics and Innovation: Review on bias in algorithmic decision making
- Open Data Institute: Data Ethics Canvas
- Etalab: How Etalab is working towards public sector algorithms accountability
- United Nations: Extreme poverty and human rights
This is not an exhaustive list of resources. If you provide tools or resources in this topic, please let us know by emailing email@example.com
- AI Now
- Ada Lovelace Institute
- Centre for Data Ethics and Innovation
- Open Data Institute
- Partnership On AI
- The Alan Turing Institute
- The Institute for Ethical AI & Machine Learning
This is not an exhaustive list of all organisations working in this area. If your organisation is working on this topic and you’d like to be included in this list, please let us know via firstname.lastname@example.org
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Other hot topics for Data 2020
- Collaboration to solve societal problems
- Competition in digital markets
- Data ethics and responsible technology
- Data infrastructure to support our economies and societies
- Misinformation, disinformation and fact checking
- Rights and ownership
- Skills, engagement and data literacy
- Trade, productivity and international innovation
- Value estimation, prioritisation and distribution