Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward
By S. Lo Piano. 2020
206 citations recorded by the source corpus.
Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. ML approaches—one of the typologies of algorithms underpinning artificial intelligence—are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AI-driven decision-making are outlined: (a) risk assessment in the criminal justice system, and (b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined.
Subfields: AI Ethics, Fairness & Society
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In AI Ethics, Fairness & Society
- Lesia Semenova
- Chudi Zhong
- Zhi Chen
- Haiyang Huang
- Selin Akgun
- Christine Greenhow
- Cynthia Rudin
- Octavio Loyola-González
Citations in the corpus
- Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “It is universally agreed that interpretability is a key element of trust for AI models (Wagstaff, 2012; Rudin and Wagstaff, 2014; Lo Piano, 2020; Ashoori and Weisz, 2019; Thiebes et al., 2020; Spiegelhalter, 2020; Brundage et al., 2020).”
- Artificial intelligence in education: Addressing ethical challenges in K-12 settings (2021, background): “Predictive systems which are powered by algorithms jeopardize students and teachers’ autonomy and their ability to govern their own life [46, 47].”