So let’s know first what is bias in artificial intelligence?

Biases find their way into the AI systems we design, and are used to make decisions by many, from governments to businesses. Bad data used to train AI can contain implicit racial, gender, or ideological biases. Bias in AI systems could erode trust between humans and machines that learn.

Now what does ‘ethical use of AI’ really mean?

The report states that this is mostly to do with how businesses collect personal data, and if they are overly reliant on machines when making crucial business decisions (especially in banking and insurance). A more ethical approach to the use of artificial intelligence could be achieved through more regulation and more transparency. People want to know when they’re being managed by an AI. To achieve this, organizations need to focus on putting the right governance structures in place, they must not only define a code of conduct based on their own values, but also implement it as an ‘ethics-by-design’ approach, and, above all, focus on informing and empowering people in how they interact with AI solutions.

Typically, bias seeps into the AI process in one of three ways: in design, data, or selection.

·       Creating the right design

Problems normally arise in the design process if those goals are not properly framed so as to guarantee fairness, since an algorithm can set parameters that encourage bias. Companies can work towards eliminating bias by avoiding a framework that is too focused on a particular company goal and making sure to build fairness into the algorithm itself.

·       Feeding in the right data

AI is an innovative process where a machine is trained by feeding it large amounts of data. But if those datasets under- or over-represent certain groups, or use out-of-date or skewed historical records or societal norms, then any outcomes will necessarily be biased. For example, if a machine is trained to identify the best college recruits based on the backgrounds of its top students, good candidates outside of these criteria could be excluded. Similarly, algorithms using hiring data to vet candidates of a certain age could unfairly eliminate qualified candidates.

·       Bias in selection

For example in healthcare they may look at weight, age, and medical history. Bias can easily infiltrate the selection process if companies put too much emphasis on certain attributes and how they interact with other data fields.

But everything has misconceptions, same way there are main 4 misconceptions about ethics and bias in AI:

1. Misconception: Engineers are only responsible for the code.

2. Misconception: Humans and computers are interchangeable.

3. Misconception: We can't regulate the tech industry.

4. Misconception: Tech is only about optimizing metrics.

There are many question comes in our mind when we talk about bias and artificial intelligence, some of them are listed below:

Q: Are there any ethical implications that businesses need to consider when introducing AI applications into their businesses?

I think that what businesses need to be mindful of is what data they feed into their AI algorithms. As we’ve seen in several unfortunate examples, if you don’t train your AI with a wide set of data it can significantly amplify bias in the end-product. For example, if your facial recognition programme is only trained on white men, then you’re going to see some unbalanced outcomes.

If businesses are going to implement these technologies, its leaders have a responsibility to ensure that the algorithms they’re creating are reflective of the world at large. This goes beyond technology and reflects the need for wider diversity across the business, from developer teams right up to the business leadership team.

Q: Do you think that AI should be regulated? If so, what should this look like?

I think that when we consider regulation, we should think of it through a lens of purpose and intent. We need to ask ourselves whether the end-usage application of a technology is “good” or not and then build an ethical framework out from there. I see this as a more sustainable approach than regulating the development of technology that could have a significantly good effect on society and the progression of humanity.

Q: Democratization of AI is a question that often comes up in discussions around the ethics of AI. Do you think it’s possible for it to become a technology that benefits everyone?

For me the answer lies in the strength of the educational curriculum and how much it will prepare today’s learners for tomorrow’s work. That isn’t to say we should focus purely on STEM to the detriment of more liberal arts. To the contrary, to ensure that the technology we create is used responsibly and for the good, we cannot lose sight of the subjects that reflect our humanity. In the next era of human-machine partnerships, at the same time as encouraging our children to count and to read, we must also encourage a diversity in their thinking. That means recognizing the importance of the arts, humanities, and social sciences in nurturing creative, critical thinkers. Core skills like emotional intelligence and moral reasoning are vital if we are to train out the bias and single-minded thinking that exists in our industry and in our AI programmers.

For more details you can wesite:

https://securityboulevard.com/2020/02/the-challenge-of-bias-in-ai-creating-ethical-guidelines/