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Ethical Use of Facial Recognition: Doing the Right Thing

Thu Apr 22 2021  ·  

Design

 ·  3 min read
Right way to use Face Recognition
Introduction

Facial recognition technology has grown enormously in both potential and accessibility over the last few years. It can help us resolve difficult problems, increase security and improve customer or workplace experiences. Last year, nearly 3,000 missing children were identified in New Delhi, India, through the use of facial recognition technology. We are seeing equally important applications in healthcare diagnosis and assistance for the blind.

However, the technology is also easy to exploit, generating risks for everything from bad choices to invasion of privacy or even violations of authority.

Many authorities have put this technology “on hold,” as it raises complex ethical dilemmas.

“There is a strong negative sentiment against the use of face recognition technology. It is being seen as an invasion of privacy and a step toward mass surveillance,” says Frank Buytendijk, Distinguished VP Analyst, Gartner.

Facial recognition precision across gender and skin tones is improving rapidly but has had deficiencies in the past. Lack of parity and accuracy across identifying characteristics can lead to inherent bias and bad decisions. These types of applications. The use of facial recognition in border protection, police surveillance, and even government mass surveillance are problematic areas where, without careful controls and human oversight, serious problems can arise.

While that does ease the risk, the opportunity cost could be high. So how can we take benefit of the capacity of facial recognition while defending against the threats? The answer lies in taking on a strong digital ethics structure that allows an organization to take advantage of the values, while also helping to guarantee we avoid the possible problems and risks. There are four key factors to a strong digital ethics structure: fair and inclusive, human accountability, trustworthiness, and adaptability.

Fair and Inclusive

First, it’s important to focus on making sure equality across all groups and the removal of bias. Microsoft has been working to continue to progress accuracy to ensure stability across genders and skin tones, and improvements are being made rapidly. Even more crucial is an “ethics by design” mindset, which spotlights diverse teams. Having different teams involved in understanding inputs, outputs, use cases, and downstream implications helps to both recognizing preferences and ensure the utility of the outcomes for all categories.

Human Accountability

There’s an increasing effect when humans and AI come together to complete a task. If law implementations were to unleash facial recognition and make execution decisions based only on hits by the algorithm, there would be a clear problem given the changing accuracy rates of these algorithms. However, when the technology is used with proper supervision and human responsibility for the ultimate decisions being made, it can be a powerful tool in solving complex problems.

Trustworthy

Openness in use is an essential starting point for making sure that we can place trust in where and how facial recognition might be used. Further, we must look to standards bodies and watchdog groups to help verify our trust. The National Institute of Standards and Technology (NIST), an agency of the US Department of Commerce, now publishes an increasingly important review of tech accuracy.

Adaptable

Ethical use of Face recognition

With better knowledge of the technology, including identifying its flaws and ongoing assessments of its precision, we can also continue to push improvements. Altering and advancing AI systems over time is a key design principle and the development can be quick when correctly planned for. One factor serving to drive this is 3rd party oversight and measurement of bias and accuracy.

Regulation

Beyond “ethics by design” and voluntary and practical adoption of a digital ethics structure for growth and use, there is a place for administrative regulation. In addition to helping to regulate risky and problematical use of the technology, regulation has the counter-intuitive potential to speed up its application and implementation. Absent regulation, the uncertainty around what should and should not be permitted and what will and will not be allowed in the future has an alarming effect. Reasonable law governing appropriate and ethical use can reduce that uncertainty.

The Next Thing Now

There is a great chance for better experiences, safety, and outcomes of all kinds through the use of facial recognition and related tech. The consequence question to thinking “What can we do?” is “What should we and should we not do?” The right method is adopting an “ethics by design” mindset and executing these solutions in the context of a digital ethics framework. This includes supporting control, measurement, and reasonable regulation to make improvements, trustworthiness in order to fasten the adoption. This way, we can both appreciate the value and get out of the risks of misuse.

References

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