Abstract
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.
Highlights
Artificial intelligence (AI) is the branch of computer science that deals with the simulation of intelligent behaviour in computers as regards their capacity to mimic, and ideally improve, human behaviour
AI resorts to ML to implement a predictive functioning based on data acquired from a given context
The strength of ML resides in its capacity to learn from data without need to be explicitly programmed (Samuel, 1959); ML algorithms are autonomous and self-sufficient when performing their learning function. This is the reason why they are ubiquitous in AI developments
Summary
Artificial intelligence (AI) is the branch of computer science that deals with the simulation of intelligent behaviour in computers as regards their capacity to mimic, and ideally improve, human behaviour. Applications in our daily lives encompass fields, such as (precision) agriculture (Sennaar, 2019), air combat and military training (Gallagher, 2016; Wong, 2020), education (Sears, 2018), finance (Bahrammirzaee, 2010), health care (Beam and Kohane, 2018), human resources and recruiting (Hmoud and Laszlo, 2019), music composition (Cheng, 2009/09), customer service (Kongthon et al, 2009), reliable engineering and maintenance (Dragicevic et al, 2019), autonomous vehicles and traffic management (Ye, 2018), social-media news-feed (Rader et al, 2018), work scheduling and optimisation (O’Neil, 2016), and several others In all these fields, an increasing amount of functions are being ceded to algorithms to the detriment of human control, raising concern for loss of fairness and equitability (Sareen et al, 2020). We will (i) detail a series of research questions around the ethical principles in AI; (ii) take stock of the production of guidelines elaborated in the field; (iii) showcase their prominence in practical examples; and (iv) discuss actions towards the inclusion of these dimensions in the future of AI ethics
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