{"id":13266,"date":"2022-08-20T14:40:53","date_gmt":"2022-08-20T09:10:53","guid":{"rendered":"https:\/\/www.allerin.com\/blog\/?p=13266"},"modified":"2022-08-19T11:53:46","modified_gmt":"2022-08-19T06:23:46","slug":"building-a-woke-ai","status":"publish","type":"post","link":"https:\/\/www.allerin.com\/blog\/building-a-woke-ai\/","title":{"rendered":"Building a “Woke” AI"},"content":{"rendered":"

\"AI<\/a>AI bias is becoming more common as the technology applications are increasing. This bias can be attributed to the aberration in the AI programs’ output due to biases in the data used for training the algorithms. Similarly, assumptions made during algorithm development can also lead to AI bias. Thus, many AI programs have been proven to discriminate against people of color, women, and other minorities.<\/p>\n

<\/h4>\n

For example, research<\/a> has shown that many facial recognition systems falsely identified Asian and African-American faces ten to a hundred times more than Caucasian ones. Similarly, another research<\/a> has shown that AI algorithms ignore female bodies in medical studies. The examples are endless. Such bias can lead to disastrous consequences and impact human lives to a great extent in a negative way. Thus, a need arises to build an ethical or “woke AI” that helps eliminate the AI bias.<\/p>\n

\"AI<\/a><\/p>\n

Building a Woke AI to Eliminate AI Bias<\/h2>\n

So, how do you build an ethical or woke AI? To build a woke AI, we need to focus on seven key factors or requirements.<\/p>\n

Transparency<\/h3>\n

Various aspects of building the AI program, such as the data, system, and algorithms, must be fully transparent. One of the simplest ways of achieving this is by implementing traceability mechanisms<\/a> that can help track and log the entire development lifecycle of the AI program. Moreover, we should also be able to understand the process and the reasons why the AI algorithms arrive at a particular decision to ensure complete transparency. This can be achieved through explainable AI<\/a>.<\/p>\n

Robustness<\/h3>\n

AI programs need to be powerful and resilient. A backup plan should be in place to ensure minimum or zero negative consequences in case something goes awry with the system.<\/p>\n

Accountability<\/h3>\n

Strict mechanisms, rules, and frameworks should be implemented to ensure accountability for AI programs and their outputs. One way to do this is through AI audits<\/a>. Audits can help assess whether the training data used is fair or not by creating hypothetical scenarios to examine the output of the AI program.<\/p>\n

Non-Discrimination<\/h3>\n

The AI program should not discriminate against any individual based on their sex, color, ethnicity, disability, and other factors throughout the entire life cycle of the program.<\/p>\n

Privacy Protection<\/h3>\n

The AI program must ensure that user data remains confidential and that only authorized individuals access the data after proper verification.<\/p>\n

Human Intervention<\/h3>\n

Proper mechanisms should be implemented to ensure that the AI systems don’t deviate from their working principles and objectives, leading to unethical outputs. This can be done by having a human oversight over the AI program at all times.<\/p>\n

Societal Well-Being<\/h3>\n

The AI programs should be developed to benefit all human beings, including future generations.<\/p>\n

Conclusion<\/h2>\n

The discussion regarding ethical or “woke” AI is headed in the right direction. AI bias could gradually reduce and cease to exist in the upcoming years. However, we must also remember that to build a \u201cwoke” AI, we must become a ‘woke’ society first. Only then can we develop truly ethical and “woke” AI systems.<\/p>\n","protected":false},"excerpt":{"rendered":"

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