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Artificial intelligence (AI) has amazing potential to change the world, and we’ve only just begun to scratch the surface. As AI matures and people move further away from distinct programming and monitoring of systems, unidentified bias (偏见) might make decisions continue for a long time that cause _______ harm for individuals and society. This bias might _______ input data or even the algorithms (算法) themselves.

All too often, data sets are incomplete and the sample represented in the data set does not _______ the population that the AI model is making predictions about — this is known as coverage bias. Some other types of bias related to input data include sampling bias, where data is not collected randomly from the target group, and participation bias, where users from certain groups _______ surveys at different rates than users from other groups. Still, another more challenging bias to identify is confirmation bias that occurs when a decision maker or analyst has a strong _______ belief or experience that affects their ability to consider alternatives. This could lead one to more strongly _______ data that confirms a preexisting belief.

Bias resulting from AI algorithms themselves, or algorithmic bias, is equally _______. One example of algorithmic bias is implicit bias or unconscious bias, where data scientists _______ make associations or assumptions based on their mental models and memories that affect data modeling decisions. Implicit bias can _______ how data is collected and classified, or how systems are designed and developed. As machines learn, their conclusions and decisions affect people. Ethical (道德的) AI must understand these impacts and create governance and testing methods to ________ mistakes and inaccuracies.

To create ethical AI, companies need to put the ________ of the individual at the center of data innovation. This means thinking about ________ rights as human rights and developing a comprehensive approach to data, including how we use AI.

Having ________ data practices for AI means having good AI governance. This governance not only focuses on data and analytics but also understands the impacts of any given analysis and makes sure it’s ________ and accurate. Good AI governance includes data responsibility as well as a commitment to transparency (透明性).

None of this will be easy, but true innovation never is. By coming together and working on the problem of bias now, before it becomes a(n) ________ force, businesses can help bring out the best AI has to offer the world.