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Algorithm and data

What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review

Giarmoleo et al. (2024)

Sub-category

Risk Domain

1.1Unfair discrimination and misrepresentation

Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and unfair representation of those groups.

"More than 20% of the contributions are centered on the ethical dimensions of algorithms and data. This theme can be further categorized into two main subthemes: data bias and algorithm fairness, and algorithm opacity."(p. 10)

Entity— Who or what caused the harm

Human

Due to a decision or action made by humans

AI system

Due to a decision or action made by an AI system

Other

Due to some other reason or is ambiguous

Intent— Whether the harm was intentional or accidental

Intentional

Due to an expected outcome from pursuing a goal

Unintentional

Due to an unexpected outcome from pursuing a goal

Other

Without clearly specifying the intentionality

Timing— Whether the risk is pre- or post-deployment

Pre-deployment

Occurring before the AI is deployed

Post-deployment

Occurring after the AI model has been trained and deployed

Other

Without a clearly specified time of occurrence

Supporting Evidence (2)

"Data bias and algorithm fairness (12.3%). This category encompasses two distinct research streams. The first one delves into the social consequences of data bias and algorithm fairness. Helberger et al. (2020) present findings from a survey of the Dutch adult population, revealing that AI-driven automated decision-making systems are perceived as fairer than human decision-makers by many respondents." "The second research stream focuses on practical methodologies to mitigate bias."(p. 11)

"Algorithm opacity (7.8%). This subtheme gives rise to two distinct strands of research. The first one explores the necessity for regulations and indications for policymakers to ensure the responsible development of AI." " The second strand entails practical methodologies to address algorithmic opacity within specific domains"(p. 11)

Part of Design of AI