Algorithmic Accountability

Algorithmic Accountability

Algorithmic Accountability Jonathan Poland

Algorithmic accountability is the concept of holding algorithms and the organizations that use them accountable for the decisions they make and the actions they take. This can be applied to algorithms, automated business rules and artificial intelligence. This accountability is important because algorithms are increasingly being used to make important decisions that affect people’s lives, such as decisions about credit, employment, and criminal justice.

Algorithmic accountability involves several key components. First, it requires that algorithms and the data they use be transparent and open to scrutiny. This means that the algorithms must be able to be understood and audited by outside parties, and that the data they use must be accessible and free from bias. Second, it requires that there be clear standards and regulations governing the use of algorithms, so that they are used in a fair and ethical manner. Finally, it requires that there be mechanisms in place to hold algorithms and the organizations that use them accountable when they make mistakes or take actions that harm people.

Overall, algorithmic accountability is an important concept in the age of increasingly sophisticated algorithms and artificial intelligence. It is critical for ensuring that algorithms are used in a fair, transparent, and accountable manner.

Magic Technology

The principle that it isn’t acceptable for management of a firm to view their own technologies as magic — whereby they understand its results but not its methods. For example, a credit card company that uses an artificial intelligence to reduce credit losses without understanding what the technology is doing to achieve this end.

Governance

The principle that the directors and governance bodies of a firm are accountable for the technologies employed by the firm. In other words, humans are accountable for technology such that technology can’t be blamed for failures or noncompliance.

Transparency

The principle that the decisions and strategies created by a technology create a human readable audit trail that is communicated to stakeholders. For example, if a government algorithm denies a driver’s license to someone the reason for this denial would be communicated to the applicant in plain language.

Compliance

The principle that technology can’t be used as an excuse or route to avoid compliance to the law. For example, a mobile app for hailing taxis that is compliant with local regulations in the markets in which it operates.

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