The Ethics of AI Image Recognition

The use of artificial intelligence (AI) for image recognition offers great potential for business transformation and problem-solving. But numerous responsibilities are interwoven with that potential. Predominant among them is the need to understand how the underlying technologies work, and the safety and ethical considerations required to guide their use.

Regulations Coming for Image, Face, and Voice Recognition?

Today, governance regulations have sprung up worldwide that dictate how an individual’s personal information is held, used and who owns it. General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are examples of regulations designed to address data and security challenges faced by consumers and the businesses that possess their associated data. If laws now apply to personal data information, can regulations governing image and facial recognition—technology that can identify a person’s face and voice, the most personal “information” we possess—be far behind? Further regulations are likely coming, but organizations shouldn’t wait to plan and direct their utilization. Businesses need to follow how this technology is being both used and misused, and then proactively apply guidelines that govern how to use it effectively, safely, and ethically.

The Use and Misuse of Technology

Many organizations use recognition capabilities in helpful and transformative ways. Medical imaging is a prime example. Through machine learning, predictive algorithms come to recognize tumors more accurately and faster than human doctors can. Autonomous vehicles use image recognition to detect road signs, traffic signals, other traffic, and pedestrians. For industrial manufacturers and utilities, machines have learned how to recognize defects in things like power lines, wind turbines, and offshore oil rigs through the use of drones. This ability removes humans from what can sometimes be dangerous environments, improving safety, enabling preventive maintenance, and increasing frequency and thoroughness of inspections. In the insurance field, machine learning helps process claims for auto and property damage after catastrophic events, which improves accuracy and limits the need for humans to put themselves in potentially unsafe conditions.

Just as most technologies can be used for good, there are always those who seek to use them intentionally for ignoble or even criminal reasons. The most obvious example of the misuse of image recognition is deepfake video or audio. Deepfake video and audio use AI to create misleading content or alter existing content to try to pass off something as genuine that never occurred. An example is inserting a celebrity’s face onto another person’s body to create a pornographic video. Another example is using a politician’s voice to create a fake audio recording that seems to have the politician saying something they never actually said.

In-between intentional beneficial use and intentional harmful use, there are gray areas and unintended consequences. If an autonomous vehicle company used only one country’s road signs as the data to teach the vehicle what to look for, the results might be disastrous if the technology is used in another country where the signs are different. Also, governments use cameras to capture on-street activity. Ostensibly, the goal is to improve citizen safety by building a database of people and identities. What are the implications for a free society that now seems to be under public surveillance? How does that change expectations of privacy? What happens if that data is hacked?

Why Take Proactive Measures?

Governments and corporate governance bodies likely will create guidelines and laws that apply to these types of tools. There are a number of reasons why businesses should proactively plan for how they create and use these tools now before these laws to come into effect.

Physical safety is a prime concern. If an organization creates or uses these tools in an unsafe way, people could be harmed. Setting up safety standards and guidelines protects people and also protects the business from legal action that may result from carelessness.

Customers demand accountability from companies that use these technologies. They expect their personal data to be protected, and that expectation will extend to their image and voice information as well. Transparency helps create trust and that trust will be necessary for any business to succeed in the field of image recognition.

Putting safety and ethics guidelines in place now, including establishing best practices such as model audits and model interpretability, may also give a business a competitive advantage by the time laws governing these tools are passed. Other organizations will be playing catch-up while those who have planned ahead gain market share over their competitors.

If you currently or plan to use image and facial recognition technologies, Cloudera Fast Forward Labs offers strategic guidance and custom application development services that can help you rapidly deliver game-changing capabilities for your business – safely and ethically. 

Bethann Noble
Bethann Noble

Director Product Marketing, Machine Learning

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