Details
| Original language | English |
|---|---|
| Article number | 27 |
| Number of pages | 19 |
| Journal | Science and engineering ethics |
| Volume | 30 |
| Issue number | 4 |
| Early online date | 18 Jun 2024 |
| Publication status | Published - Aug 2024 |
Abstract
Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine’s behaviour and therefore struggle to identify an agent that can be held accountable. However, most of today’s AI is based on machine-learning technology that does not act on its own, but rather serves as a decision-support tool, automatically analysing data to help human agents make better decisions. I argue that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems. Namely, they pose a problem for what we might call “decision ownership”: they make it difficult to identify human agents to whom we can attribute value-judgements that are reflected in decisions. Drawing on recent philosophical literature on responsibility and its various facets, I argue that this is primarily a problem of attributability rather than of accountability. This particular responsibility problem comes in different forms and degrees, most obviously when an AI provides direct recommendations for actions, but also, less obviously, when it provides mere descriptive information on the basis of which a decision is made.
Keywords
- Accountability, Agency, Artificial intelligence, Attributability, Decision-support systems, Machine learning, Responsibility
ASJC Scopus subject areas
- Social Sciences(all)
- Health(social science)
- Nursing(all)
- Issues, ethics and legal aspects
- Medicine(all)
- Health Policy
- Business, Management and Accounting(all)
- Management of Technology and Innovation
Sustainable Development Goals
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In: Science and engineering ethics, Vol. 30, No. 4, 27, 08.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Owning Decisions
T2 - AI Decision-Support and the Attributability-Gap
AU - Zeiser, Jannik
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024/8
Y1 - 2024/8
N2 - Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine’s behaviour and therefore struggle to identify an agent that can be held accountable. However, most of today’s AI is based on machine-learning technology that does not act on its own, but rather serves as a decision-support tool, automatically analysing data to help human agents make better decisions. I argue that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems. Namely, they pose a problem for what we might call “decision ownership”: they make it difficult to identify human agents to whom we can attribute value-judgements that are reflected in decisions. Drawing on recent philosophical literature on responsibility and its various facets, I argue that this is primarily a problem of attributability rather than of accountability. This particular responsibility problem comes in different forms and degrees, most obviously when an AI provides direct recommendations for actions, but also, less obviously, when it provides mere descriptive information on the basis of which a decision is made.
AB - Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine’s behaviour and therefore struggle to identify an agent that can be held accountable. However, most of today’s AI is based on machine-learning technology that does not act on its own, but rather serves as a decision-support tool, automatically analysing data to help human agents make better decisions. I argue that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems. Namely, they pose a problem for what we might call “decision ownership”: they make it difficult to identify human agents to whom we can attribute value-judgements that are reflected in decisions. Drawing on recent philosophical literature on responsibility and its various facets, I argue that this is primarily a problem of attributability rather than of accountability. This particular responsibility problem comes in different forms and degrees, most obviously when an AI provides direct recommendations for actions, but also, less obviously, when it provides mere descriptive information on the basis of which a decision is made.
KW - Accountability
KW - Agency
KW - Artificial intelligence
KW - Attributability
KW - Decision-support systems
KW - Machine learning
KW - Responsibility
UR - http://www.scopus.com/inward/record.url?scp=85196270364&partnerID=8YFLogxK
U2 - 10.1007/s11948-024-00485-1
DO - 10.1007/s11948-024-00485-1
M3 - Article
C2 - 38888795
AN - SCOPUS:85196270364
VL - 30
JO - Science and engineering ethics
JF - Science and engineering ethics
SN - 1353-3452
IS - 4
M1 - 27
ER -