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Machine Learning, AI, Bias. Why Does It Matter?

What is Artificial Intelligence?

Artificial intelligence (AI in short) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.

Pros of Machine learning, AI

There is no doubt in saying that technology is an essential part of the development and growth of humans. A thin line or mistake leads to disruption or destruction.

Machines make decisions based on previous data records. With algorithms, the chances of errors are reduced. This is an achievement, as solving complex problems requires difficult calculations that can be done without any error. Business organizations use digital assistants to interact with their users, this helps them to save an ample amount of time. The demand for user’s businesses is fulfilled and thus they don’t have to wait. They are programmed to give the best possible assistance to a user.

Secondly, highly advanced organizations have digital assistants that help them to interact with the users and save the need for human resources. Machines think faster than humans and can perform various functions at the same time. Machines can be employed to carry out dangerous tasks and their parameters are adjusted. This is not possible with humans as their speed and time can’t be calculated based on parameters.

Cons of Machine Learning, AI

AI indeed comes with a high cost, but there is no such thing as a free lunch too. It requires huge costs as it is a complex machine. Apart from the installation cost, its repair and maintenance also require huge costs. The software programs need frequent up-gradation and cater to the needs of the changing environment.

No matter how smart a machine becomes, it can never replicate a human. Machines are rational but very inhuman as they don’t possess emotions and moral values. They don’t know what is ethical and what’s legal and because of this, don’t have their judgment making skills. They do what they are told to do and therefore the judgment of right or wrong is nil for them. If they encounter a situation that is unfamiliar to them then they perform incorrectly or else break down in such situations.

Machines can’t be creative. They can only do what they are being taught or commanded. Though they help in designing and creating, they can’t match the power of the human brain.

This one is the riskiest and can have severe effects. With capital intensive technologies, human-intensive requirements have decreased in some industries. If in the future, human beings don’t add to their skills, then in no time, we can see that they will be replaced with machines. The major issue of the GDP being stagnant or not growing at the expected rate is unemployment. People don’t possess the required skills that are in demand. There are a huge demand and supply gap because of this.

AI + Bias

AI bias is an anomaly in the output of machine learning algorithms. These could be due to the prejudiced assumptions made during the algorithm development process or prejudices in the training data

An example is with the dream of automating the recruiting process, Amazon started an AI project in 2014. Their project was solely based on reviewing job applicants’ resumes and rating applicants by using AI-powered algorithms so that recruiters don’t spend time on manual resume screen tasks. However, by 2015, Amazon realized that their new AI recruiting system was not rating candidates fairly and it showed bias against women.

Another example occurred in the Facebook scandal. In 2019, Facebook was allowing its advertisers to intentionally target adverts according to gender, race, and religion. For instance, women were prioritized in job adverts for roles in nursing or secretarial work, whereas job ads for janitors and taxi drivers had been mostly shown to men, in particular men from minority backgrounds.

Implications Of Bias Towards Minority Groups

Technology, primarily artificial intelligence, can be used as an instrument of discrimination against minorities. With recent Black Lives Matter protests taking place across the US, discussions, and debates are now underway over how racism is institutionalized and directly influenced by the inherent biases presiding in artificial intelligence.

Facial recognition is an increasingly controversial aspect of technologies that has a controversial track record when it comes to racial and gender bias, due to systems consistently misidentifying non-white faces.

Evidently, we would fundamentally need principles and standards, governing bodies, and people voting on and restricting algorithms, as well as precautions, something similar to the F.D.A. Even considering these aspects of restrictions of artificial intelligence, that’s only one component of the equation.

Will AI be completely unbiased?

Technically, yes. An AI system can be as good as the quality of its input data. If you can clean your training dataset from conscious and unconscious assumptions on race, gender, or other ideological concepts, you can build an AI system that makes unbiased data-driven decisions.

However, in the real world, we don’t expect AI to ever be completely unbiased any time soon due to the same argument we provided above. AI can be as good as data and people are the ones who create data. There are numerous human biases and the ongoing identification of new biases is increasing the total number constantly. Therefore, it may not be possible to have a completely unbiased human mind so does the AI system. After all, humans are creating biased data while humans and human-made algorithms are checking the data to identify and remove biases.

What we can do for AI bias is to minimize it by performing tests on data and algorithms and applying other best practices.


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