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Algorithmic bias does not only include protected categories, but can also concern characteristics less easily observable or codifiable, such as political viewpoints. In these cases, there is rarely an easily accessible or non-controversial ground truth, and removing the bias from such a system is more difficult. [148]
Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e.g., gender, ethnicity, sexual orientation, or disability).
In 2023, Google's photos algorithm was still blocked from identifying gorillas in photos. [10] Another prevalent example of representational harm is the possibility of stereotypes being encoded in word embeddings, which are trained using a wide range of text.
In 2016, the World Economic Forum claimed we are experiencing the fourth wave of the Industrial Revolution: automation using cyber-physical systems. Key elements of this wave include machine ...
The consequences of algorithmic bias could mean that Black and Hispanic individuals end up paying more for insurance and experience debt collection at higher rates, among other financial ...
Additionally, the use of algorithms by governments to act on data obtained without consent introduces significant concerns about algorithmic bias. Predictive policing tools, for example, utilize historical crime data to predict “risky” areas or individuals, but these tools have been shown to disproportionately target minority communities. [15]
The Health Information National Trends Survey reports that 75% of Americans go to the internet first when looking for information about health or medical topics. YouTube is one of the most popular ...
The most predominant view on how bias is introduced into AI systems is that it is embedded within the historical data used to train the system. [24] For instance, Amazon terminated their use of AI hiring and recruitment because the algorithm favored male candidates over female ones. This was because Amazon's system was trained with data ...