algorithmic impact assessment
In plain wordsA structured review, done before and during the use of an automated system, of whom it could affect and how — such as unfair treatment or privacy harms — and what will be done about it.
A structured process, often mandated for public agencies or high-risk uses, in which an organization documents an automated system's purpose, data, logic and affected populations; identifies foreseeable harms such as discrimination, privacy intrusion, errors and effects on rights; and records mitigations, monitoring and avenues of redress, usually before deployment and at regular intervals afterward. Modeled on environmental and privacy impact assessments, AIAs range from self-administered questionnaires to externally reviewed reports with public consultation. Canada's Directive on Automated Decision-Making (2019) requires federal institutions to complete an AIA questionnaire that assigns an impact level from I to IV, with safeguards scaled to the level; the original 2024 Colorado AI ActColorado AI Act, prop. n.: A Colorado law, first passed in 2024, governing AI used to make important decisions about people, such as hiring, lending and housing. In 2026 it was rewritten as a narrower law that starts in 2027. would have required deployers of high-risk systems to complete annual impact assessments, a duty dropped when the law was rewritten in 2026 before taking effect; and the EU AI ActEU AI Act, prop. n.: The European Union's law on artificial intelligence, the first broad AI law anywhere: it bans some uses, sets strict checks for risky ones and puts duties on the makers of the biggest AI models. requires a fundamental rights impact assessmentfundamental rights impact assessment, n.: A check that certain organizations must make before using a high-risk AI system, writing down whose rights it could affect, how, and what they will do to prevent harm. from certain deployers. Critics warn that assessments become box-ticking unless they are published and independently checked.
The city published its algorithmic impact assessment before switching on the benefits-eligibility model.
Origin
Proposed as an accountability framework for government use by the AI Now Institute (Dillon Reisman, Jason Schultz, Kate Crawford and Meredith Whittaker) in April 2018, drawing on the data protection impact assessment of the EU's General Data Protection Regulation (2016) and on environmental impact statements under the US National Environmental Policy Act (1970).
Related
- algorithmic accountabilityalgorithmic accountability, n.: The idea that the people and organizations behind automated decision systems must be able to explain and justify those systems' effects, answer for the harm they cause, and fix it.
- fundamental rights impact assessmentfundamental rights impact assessment, n.: A check that certain organizations must make before using a high-risk AI system, writing down whose rights it could affect, how, and what they will do to prevent harm.
- AI auditAI audit, n.: An independent check of an AI system, or of the company using it, against agreed standards — for example, testing whether a hiring tool treats women and men fairly.
- automated decision-makingautomated decision-making, n.: When a computer system, rather than a person, makes or effectively makes a decision about someone — such as approving credit, screening job applicants or flagging possible welfare fraud.
- Colorado AI ActColorado AI Act, prop. n.: A Colorado law, first passed in 2024, governing AI used to make important decisions about people, such as hiring, lending and housing. In 2026 it was rewritten as a narrower law that starts in 2027.
- NIST AI Risk Management FrameworkNIST AI Risk Management Framework, prop. n.: A free, voluntary guide from the US government's standards agency that helps organizations spot, measure and reduce the risks of the AI systems they build or use.
Sources
- Reisman, D., Schultz, J., Crawford, K. & Whittaker, M. (2018). Algorithmic Impact Assessments: A Practical Framework for Public Agency Accountability. AI Now Institute.
Defined in law or standards
Government of Canada, Directive on Automated Decision-Making (2019), s. 6.1
Mentioned in
AI auditAI audit, n.: An independent check of an AI system, or of the company using it, against agreed standards — for example, testing whether a hiring tool treats women and men fairly., algorithmic accountabilityalgorithmic accountability, n.: The idea that the people and organizations behind automated decision systems must be able to explain and justify those systems' effects, answer for the harm they cause, and fix it., automated decision-makingautomated decision-making, n.: When a computer system, rather than a person, makes or effectively makes a decision about someone — such as approving credit, screening job applicants or flagging possible welfare fraud., Colorado AI ActColorado AI Act, prop. n.: A Colorado law, first passed in 2024, governing AI used to make important decisions about people, such as hiring, lending and housing. In 2026 it was rewritten as a narrower law that starts in 2027., responsible AIresponsible AI, n.: An umbrella name for the practices organizations use to keep their AI systems fair, safe, private and accountable, from the first design meeting to everyday use., AI Now InstituteAI Now Institute, prop. n.: A research and advocacy group that examines who is harmed by AI and argues that the deeper problem is the power of a few big technology companies, which it wants curbed by law., automated employment decision toolautomated employment decision tool, n.: Software that scores, ranks or screens job candidates or employees. New York City requires employers using such tools to have them checked for bias every year and to tell applicants., fundamental rights impact assessmentfundamental rights impact assessment, n.: A check that certain organizations must make before using a high-risk AI system, writing down whose rights it could affect, how, and what they will do to prevent harm., Kate CrawfordKate Crawford, prop. n.: An Australian scholar who studies the social, political and environmental costs of AI, co-founded the AI Now Institute, and wrote 'Atlas of AI'.