Ethical ai at work: The social contract for artificial intelligence and its implications for the workplace psychological contract
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI models might be vulnerable to attacks by adversaries with malicious intentions.
“It is widely recognized that AI models might be vulnerable to attacks by adversaries with malicious intentions.”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [11]
Many current AI systems are vulnerable to imperceptible attacks.
“many current AI systems are found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection.”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [11]
The detection and mitigation of adversarial inputs is considered a means to defend against evasion attacks and data poisoning attacks.
“The detection and mitigation of adversarial inputs is also considered to be a means to defend against evasion attacks and data poisoning attacks [304, 213, 11].”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [11]
Disclosure of detailed algorithmic mechanisms can lead to the risk of targeted hacking.
“Disclosure of detailed algorithmic mechanisms can also lead to the risk of targeted hacking [11].”
From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [11]
Cites
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · see citing works
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774 · see citing works
Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732 · see citing works
The Ethics of AI Ethics: An Evaluation of Guidelines
Hagendorff, T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09517-8 · Cited by 1,911 · see citing works
Technological frames: making sense of information technology in organizations
Orlikowski, W. J., & Gash, D. C. (1994) · ACM Transactions on Information Systems · doi:10.1145/196734.196745 · Cited by 1,736 · see citing works
A Unified Framework of Five Principles for AI in Society
Floridi, L., & Cowls, J. (2019) · Harvard Data Science Review · doi:10.1162/99608f92.8cd550d1 · Cited by 1,631 · cited 5× · see citing works
“Given their greater simplicity and generality, we use Floridi and Cowls’ (2019) five principles for ethical AI in our framework.”
BackgroundMethodInfluentialArtificial Intelligence in Human Resources Management: Challenges and a Path Forward
Tambe, P., Cappelli, P., & Yakubovich, V. (2019) · California Management Review · doi:10.1177/0008125619867910 · Cited by 1,609 · see citing works
Manager-employee psychological contracts: enter the smartphone
Obushenkova, E., Plester, B., & Haworth, N. (2018) · Employee Relations · doi:10.1108/er-02-2017-0040 · Cited by 26 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612 · see citing works
The ethical use of artificial intelligence in human resource management: a decision-making framework
Bankins, S. (2021) · Ethics and Information Technology · doi:10.1007/s10676-021-09619-6 · Cited by 181 · see citing works
Self-citation
- Authors
- Bankins, S., & Formosa, P.
- Affiliations
- Macquarie University (AU)
- Corresponding author
- Bankins, Sarah
- Venue
- Redefining the Psychological Contract in the Digital Era
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 55-72
- OpenAlex
- W3131944092
- Cited by
- 19
- References
- 8
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 5 · 2023: 2 · 2021: 1
- FWCI
- 8.24
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Springer eBooks (publishedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Job Satisfaction and Organizational Behavior
- Keywords
- ethical AI, artificial intelligence in the workplace, social contract theory, psychological contract, integrative social contracts theory, beneficence, non-maleficence, autonomy, justice, explainability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review