As people continue exploring ways to use artificial intelligence (AI) in modern society, there’s an increasing concern about ensuring all the current, potential and future applications operate ethically. Many professionals have devoted themselves to furthering ethical AI principles by developing guidelines, best practices and other resources for the industry at large to use.
Data science practices will inevitably be altered, as well.
It May Increase Awareness of Black-Box Algorithms
Many AI algorithms used by data scientists and others are the black-box type. That means people cannot see how an artificial intelligence tool made its decision. The unfortunate issue with these unexplainable algorithms is that many industries and companies already use them for applications that could alter someone’s life.
Some companies have tackled the problem by designing dedicated tools. Such products are steps in the right direction, but there’s still substantial progress to make.
The lack of decision-making insight is one of the main issues that could cause ethical problems. Banks use black-box algorithms when representatives crunch data to determine whether to offer a customer a loan. Such algorithms could also flag suspected fraud on an account, which could be advantageous. What if it was a false alarm, though — and the whole ordeal blocks the affected customer from their account for months?
The issues of how and when banks can use these algorithms fall outside regulators’ authority, so it’s understandable why people are wary.
Some have similar uncertainties about using AI in medical applications, such as diagnostic support. Evidence already shows some artificial intelligence tools can diagnose illnesses as effectively as doctors with years of experience.
However, one of the concerns with black-box algorithms is they do not allow physicians to adequately explain medical decisions to patients. Thus, some people familiar with the matter believe doctors should only use this type of AI for decision support or to treat patients in genuinely dire circumstances.
Many data scientists respond to these concerns by working on explainable AI algorithms. They allow people to interpret and trust results because they can see how the artificial intelligence tool reached that conclusion. People currently working in data science or aspiring to enter the field soon should expect explainable AI to continue significantly impacting their work.
It Will Require Ongoing Work to Reduce Bias
Humans have many internal biases that can affect how they see the world, so it’s only natural that the AI algorithms people build contain them, too. Data scientists also encounter numerous biases when gathering data to create algorithms. Those often occur because of limitations in the data available for someone to collect.
The lack of bias is a fundamental part of progress in ethical AI. Discrimination can cause extraordinary problems in situations where people are compared to each other, such as while applying for a job or attending an audition for a place at an arts college.
There are many accessible ways for human resources professionals to apply AI ethically in the workplace. Consider how 41% of companies budget for employees to receive in-person training. Algorithms can handle vast quantities of data, which can streamline the process.
A human resources manager might provide information about team members’ past performance on training modules, their overall experience in their roles and previous knowledge gaps to an algorithm. The results could help a trainer understand which areas to cover or skip in an upcoming session.
At the same time, anyone using AI for employee-related applications must not immediately buy into some of the fantastic claims they might hear about the technology. For example, some people hoped AI tools could result in more diverse workplaces if used to support hiring. However, a Cambridge University team found the opposite is likely true. They said the technology could result in more uniform workplaces.
Data scientists can play important roles in reducing bias and reminding people it will always be present despite progress to conquer it. Such efforts will be critical for forming the foundations of ethical AI.
It Highlights the Need for Transparency
Many consumers find themselves in a complicated relationship with AI. They might like how it provides personalized recommendations while shopping but feel wary about what companies do with that data and wonder if the information is handled responsibly.
One of the key takeaways from a 2023 study was that 51% of respondents felt AI helped them have better retail experiences. However, 63% wanted retailers to better balance offering personalization and collecting their data. Elsewhere, a 2023 Gallup poll revealed that 79% of respondents had little or no trust that businesses would use AI responsibly.
These statistics show the need for companies to have and follow ethical AI principles. Data scientists can help create them. Relatedly, consumers must have clear details about how, why and when businesses use their information. The option to provide or revoke access at any time also offers more control over that first-party information.
Ethical AI Is Necessary
Artificial intelligence algorithms are powerful, and they’ve already changed how many people do things. However, as the use cases grow, so does the potential for individuals to purposefully or unintentionally utilize AI unethically. Studying, testing and otherwise investing in ethical AI-related work will reduce misuse that could cause harm and widespread ramifications.
About the Author
April Miller is a senior IT and cybersecurity writer for ReHack Magazine who specializes in AI, big data, and machine learning while writing on topics across the technology realm. You can find her work on ReHack.com and by following ReHack’s Twitter page.
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