Risk
When harm is assessed using statistical approaches, impacts on people with disabilities may be dismissed as anecdotal or not statistically significant enough to act on. As a result, mitigation approaches that depend on these methods may be more likely to overlook or fail to address harms affecting people whose experiences fall outside common patterns.
Mitigation
Collect and analyze individual reports of harm alongside statistical data, using tools such as an incident database. This approach creates a structured way to document real-world experiences, including detailed descriptions of what happened, who was affected, and what the impact was. Including anecdotal evidence helps ensure that lived experiences are treated as valid forms of evidence, not just as isolated incidents. It can also support earlier detection of harm, especially in situations where waiting for large amounts of data could delay action.
Illustrative Examples
Education
System-wide issues dismissed as one-off student complaints
When students report issues with tools (e.g., speech recognition or captioning errors), these may be viewed as individual problems rather than part of a broader pattern of errors. This can reduce the likelihood of system-wide fixes. Over time, similar issues may be reported by multiple students but treated separately, which can prevent patterns from being identified. As a result, the tool may continue to perform poorly for certain users without triggering meaningful updates or improvements.
Employment
Discriminatory hiring patterns explained away as normal variation
Patterns such as more frequent rejection of job candidates with non-standard work histories may be explained away as normal variation in the data. This can prevent employers from recognizing and correcting barriers linked to disability. Over time, each decision may be treated as an isolated case rather than part of a broader issue. As a result, qualified candidates may continue to be overlooked without triggering review or change in the system.
Healthcare
Rare but serious harms left unaddressed
An AI diagnostic system can make more mistakes for people with less common conditions, but these errors may be considered too rare to address. The impact may be serious or life-threatening, even a small number of missed or incorrect diagnoses may delay treatment, lead to inappropriate care, or worsen existing conditions. Over time, treating these errors as rare may allow the same risks to continue for others with similar needs without being corrected.
Services
Ongoing access issues treated as isolated cases
When a benefits system incorrectly denies or delays applications from people with disabilities at slightly higher rates, it may not trigger action if the difference is seen as too small within the data sets. Being treated as anecdotal isolated cases can allow the same issue to continue over time, creating continual barriers to access without leading to system-wide fixes.