Human Factors in Machine Learning focuses on understanding how humans interact with and are affected by ML systems, with the goal of designing systems that are user-friendly, safe, and effective.

While visual appeal is important in general user interface design, it is not a specific challenge unique to ML systems.

Automation bias refers to the tendency of users to place too much trust in automated systems, even when they are not fully reliable.

All of the above practices can help to mitigate the risk of automation bias by making users more aware of the system's limitations and by encouraging them to be more critical of its output.

Illusion of control bias refers to the tendency of users to believe that they have more control over a system than they actually do, which can lead to them becoming overly reliant on the system and failing to use their own judgment and critical thinking skills.

All of the above practices can help to mitigate the risk of illusion of control bias by making users more aware of the system's limitations and by encouraging them to be more critical of its output.

System resistance refers to the phenomenon where users develop a negative attitude towards an ML system and become unwilling to use it, often due to a lack of trust or perceived usefulness.

All of the above practices can help to mitigate the risk of system resistance by making users more aware of the system's benefits and limitations, by encouraging them to try it out, and by making it easy for them to use.

Overconfidence bias refers to the tendency of users to overestimate their own abilities and knowledge, which can lead them to develop unrealistic expectations about the capabilities of an ML system.

All of the above practices can help to mitigate the risk of overconfidence bias by making users more aware of the system's limitations and by encouraging them to be more critical of its output.

Framing effect refers to the phenomenon where users are more likely to trust the output of an ML system if it is presented in a visually appealing or persuasive manner, even if the underlying data or algorithm is the same.

All of the above practices can help to mitigate the risk of framing effect by making users more aware of the system's limitations and by encouraging them to be more critical of its output.

Anthropomorphism bias refers to the tendency of users to attribute human-like qualities to non-human entities, such as ML systems, which can lead them to be more trusting of the system's output.

All of the above practices can help to mitigate the risk of anthropomorphism bias by making users more aware of the system's limitations and by encouraging them to be more critical of its output.

Authority bias refers to the tendency of users to trust the output of an ML system more if it is presented in a confident or assertive manner, even if the underlying data or algorithm is the same.

All of the above practices can help to mitigate the risk of authority bias by making users more aware of the system's limitations and by encouraging them to be more critical of its output.