1.3Unequal performance across groups

11.01.03Erasing social groups

people, attributes, or artifacts associated with specific social groups are systematically absent or under-represented... Design choices [143] and training data [212] influence which people and experiences are legible to an algorithmic system

11.03.00Quality-of-Service Harms

"These harms occur when algorithmic systems disproportionately underperform for certain groups of people along social categories of difference such as disability, ethnicity, gender identity, and race."

11.03.01Alienation

Alienation is the specific self-estrangement experienced at the time of technology use, typically surfaced through interaction with systems that under-perform for marginalized individuals

11.03.02Increased labor

increased burden (e.g., time spent) or effort required by members of certain social groups to make systems or products work as well for them as others

11.03.03Service/benefit loss

degraded or total loss of benefits of using algorithmic systems with inequitable system performance based on identity

13.01.03Disparate Performance

"In the context of evaluating the impact of generative AI systems, disparate performance refers to AI systems that perform differently for different subpopulations, leading to unequal outcomes for those groups."

16.01.04Lower performance for some languages and social groups

"LMs are typically trained in few languages, and perform less well in other languages [95, 162]. In part, this is due to unavailability of training data: there are many widely spoken languages for which no systematic efforts have been made to create labelled training datasets, such as Javanese which is spoken by more than 80 million people [95]. Training data is particularly missing for languages that are spoken by groups who are multilingual and can use a technology in English, or for languages spoken by groups who are not the primary target demographic for new technologies."

17.01.04Lower performance for some languages and social groups

"LMs perform less well in some languages (Joshi et al., 2021; Ruder, 2020)...LM that more accurately captures the language use of one group, compared to another, may result in lower-quality language technologies for the latter. Disadvantaging users based on such traits may be particularly pernicious because attributes such as social class or education background are not typically covered as ‘protected characteristics’ in anti-discrimination law."

18.01.02Unfair capability distribution

"Performing worse for some groups than others in a way that harms the worse-off group"

30.03.00Fairness

Avoiding bias and ensuring no disparate performance

30.03.04Disparate Performance

The LLM’s performances can differ significantly across different groups of users. For example, the question-answering capability showed significant performance differences across different racial and social status groups. The fact-checking abilities can differ for different tasks and languages

39.08.00Fairness

This challenge appears when the learning model leads to a decision that is biased to some sensitive attributes... data itself could be biased, which results in unfair decisions. Therefore, this problem should be solved on the data level and as a preprocessing step

42.14.00Fairness

"Impartial and just treatment without favouritism or discrimination."

47.02.09Bias and discrimination (value embedding)

"Generative AI models may also be subject to the “value embedding” phenomenon.361 “Value embedding” refers to the fact that developers of generative AI models strive to minimize biased outputs by retraining their models based on normative values.362 Contemporary state-of- the-art models not only reflect the values embedded within their training data, they also undergo additional fine-tuning that follows a set of chosen rules and principles. Due to the absence of universally accepted standards, developers bear the responsibility of making decisions on sensitive issues. These practices lead to concerns that a developer’s ideology and vision of the world are embedded in the model. This generates a risk that the model incorporates values that are either unrepresentative of certain segments of the population or that offer a static, oversimplified reflection of global cultural norms and evolving social views."

52.03.02Ideological Homogenization from Value Embedding

"The increasing integration of general purpose AI models into every-day life raises concerns around their embedded normative values. The reach of a small number of AI models to a large number of people around the world can make these value judgements unprecedently impactful, potentially leading to increased ideological homogenization."

65.23.04Impact on affected communities

"It is important to include the perspectives or concerns of communities that are affected by model outcomes when designing and building models. Failing to include these perspectives makes it difficult to understand the relevant context for the model and to engender trust within these communities."

66.06.03Unfair capability distribution

"Performing worse for some groups than others in a way that harms the worse-off group"