Accountable Artificial Intelligence: Holding Algorithms to Account
Artificial intelligence algorithms govern in subtle, yet fundamental ways, the way we live and are transforming our societies. The promise of efficient, low‐cost or ‘neutral’ solutions harnessing the potential of big data has led public bodies to adopt algorithmic systems in the provision of public services.
- Madalina Busuioc
- 27 August 2020
As AI algorithms have permeated high‐stakes aspects of our public existence – from hiring and education decisions, to the governmental use of enforcement powers (policing) or liberty‐restricting decisions (bail and sentencing), this necessarily raises important accountability questions: What accountability challenges do AI algorithmic systems bring with them, and how can we safeguard accountability in algorithmic decision‐making?
Evidence for Practice
- The article provides public sector practitioners with insight into the distinct accountability challenges associated with the use of AI systems in public sector decision-making.
- It digests and explicitly links technical discussions on black-box algorithms as well as explainable AI and interpretable models – different approaches aimed at model understandability – to public accountability considerations relevant for public bodies.
- Provides specific policy recommendations to securing algorithmic accountability – prominent among these, the importance of giving preference to transparent, interpretable models in the public sector over black-box alternatives (whether in a proprietary or in a technical sense i.e. deep learning models).
- This will become critical to administrators‟ ability to maintain oversight of system functioning as well as to their ability to discharge their account-giving duties to citizens for algorithmic decision-making.
We have seen that challenges arising from algorithmic use give rise to deficits that strike at the heart of accountability processes: compounded informational problems, the absence of adequate explanation or justification of algorithm functioning and ensuing difficulties with diagnosing failure and securing redress. At its core, accountability is about answerability yet current AI algorithmic use comes with serious challenges to our collective ability to interrogate (and challenge) algorithmic outcomes.