Artificial Intelligence (AI) has rapidly changed everything we do, offering numerous benefits across various industries. From healthcare to finance, from transportation to entertainment, AI has made its presence felt, promising to usher in a new era of innovation and efficiency. However, as the power and influence of AI continue to grow, so do concerns about its ethical implications and accountability. The battle for ethical innovation and accountability in AI is an urgent and complex challenge that demands our attention.
AI concerns go beyond the mere identification of AI bias; it delves into a broader quest for a profound reconsideration of the very foundations on which AI systems are built. It is important to raise the alarm about the current trend in the AI domain, where technology companies are gaining the upper hand in shaping the regulations that should oversee their operations. This trend bears an unsettling resemblance to past errors that permitted the proliferation of biased and oppressive technology.
Leading the charge in this battle are two Black women whose distinct journeys converge at the core of their convictions: the urgent need to rectify the biases deeply embedded in the AI landscape. Both Joy Buolamwini and Ruha Benjamin have authored books that shed light on this pressing issue.
They share a common realization: the disconcerting fact that commercial facial recognition systems consistently exhibit failure in recognizing Black and brown faces. Buolamwini’s research, in particular, placed a spotlight on the struggles faced by Black women in this regard.
“I decided one way to humanize AI biases and make the topic more mainstream than an academic paper was to test the faces of the Black Panther cast. Since my research had shown that the systems I tested worked worst on the faces of darker-skinned females, I decided to focus on the faces of the women of Wakanda: Lupita Nyong’o as Nakia, Letitia Wright as Shuri, Angela Bassett as Queen Ramonda, and Danai Gurira as fearless General Okoye,” Buolamwini said.
“I brought on Deborah Raji as my research intern to carry out a small-scale audit running the Black Panther cast’s faces across the AI systems of five companies. This exploration became known as the Black Panther Face Scorecard project. The project revealed some commonalities with my own experience. Like me, some of their faces were misgendered, not detected at all, or in some cases mis-aged. Angela Bassett, who was in her late 50s at the time of the photo, was estimated by IBM’s system to be between 18 and 24 years old. (Maybe not all algorithmic bias was that bad.)”
Her groundbreaking findings prompted a seismic shift within tech behemoths like Google, IBM, and Microsoft. They were compelled to confront and rectify the inherent biases within their technologies, and in doing so, they distanced themselves from providing these flawed systems to law enforcement, whose use of the technology led to dire consequences. And the list of concerns and consequences keeps growing.
“What concerns me is we’re giving so many companies a free pass, or we’re applauding the innovation while turning our head [away from the harms],” Buolamwini says.
Ethical Dilemmas in AI
As AI systems become increasingly autonomous and capable, they raise ethical dilemmas across various domains. Here are a few key areas where these dilemmas become evident:
Bias and Fairness: AI algorithms can inadvertently perpetuate bias, discrimination, and unfairness. They learn from historical data, which may contain biases, and replicate those biases in their decision-making processes. This bias can manifest in areas like hiring practices, lending decisions, and criminal justice.
Privacy Concerns: AI systems often collect, analyze, and store massive amounts of data, raising significant privacy concerns. The use of personal data for surveillance, advertising, or profiling has the potential to infringe upon individuals’ rights and freedoms.
Autonomy and Accountability: As AI systems become more autonomous, the question of accountability becomes paramount. When a self-driving car causes an accident, who is responsible—the manufacturer, the software developer, or the owner of the vehicle?
Transparency and Explanation: Many AI systems operate as “black boxes,” making it challenging to understand their decision-making processes. The lack of transparency and explanation can hinder our ability to trust and regulate these systems effectively.
Job Displacement: The rise of AI and automation threatens job displacement in various industries. The ethical dilemma here lies in ensuring a just transition for workers whose jobs become obsolete due to AI.
Security Risks: AI can be weaponized by malicious actors to conduct cyberattacks, misinformation campaigns, and other nefarious activities. Ensuring the security of AI systems is an ethical imperative.
Numerous entities, including the Biden-Harris Administration, have taken proactive steps to formulate ethical guidelines for AI development. These initiatives involve securing voluntary commitments from prominent artificial intelligence companies to address the potential risks associated with AI technology. These ethical frameworks serve as a comprehensive set of principles and guidelines aimed at guiding the responsible advancement of AI, with a particular focus on essential elements such as fairness, transparency, and accountability.
Another vital aspect of ethical innovation in AI is the use of diverse and inclusive data. To counteract bias within AI systems, it is crucial to ensure that these systems are trained on datasets that represent a wide range of backgrounds and perspectives. Efforts are being made to collect more representative data and create tools that can identify and rectify bias within existing datasets.
The drive for ethical AI also involves ongoing research to make AI systems more explainable. This entails the development of models capable of providing clear and understandable explanations for their decisions, thereby enhancing their transparency and accountability.
AI audits, modeled after financial audits, are a valuable tool to ensure compliance with ethical guidelines. These audits involve a detailed examination of the data used, the algorithms employed, and the decision-making processes of AI systems. Their purpose is to identify and rectify any ethical concerns that may arise during the AI development process.
Additionally, governments and regulatory bodies are actively working to create laws and regulations that govern the use of AI. These measures aim to address crucial issues such as data privacy, bias, and accountability. Ultimately, they seek to provide a robust legal framework for the ethical development and deployment of AI, ensuring that AI technologies are harnessed in a manner that is consistent with societal values and norms.
In her book “Race After Technology,” Ruha Benjamin delves into the intricate interplay between technology and race, shedding light on how emerging technologies have the potential to either reinforce or challenge established racial disparities in society. Through her perceptive analysis, Benjamin investigates the role of new technologies in perpetuating or disrupting existing racial inequalities, emphasizing that technology is far from being a neutral force. Instead, it acts as a mirror that reflects and amplifies social, cultural, and political biases. Benjamin illustrates this through examples of biased algorithms, discriminatory surveillance practices, and the perpetuation of racial biases, especially in the realms of facial recognition, predictive policing, and employment screening.
Benjamin’s motivation to act was sparked by a viral video depicting a soap dispenser that failed to function for a person with darker skin while working seamlessly for someone with lighter skin. The underlying reason for this disparity was the absorption of light by darker skin tones. Although this incident might appear minor, it prompted her to contemplate the existence of other instances of what she termed “racist robots” and the potential adverse impacts of technology on people of color. This concern extended beyond simple devices to encompass more intricate systems, such as those within healthcare, criminal justice, and education, where people of color could face systemic challenges and biases.
Benjamin says, “A lot of these institutions are outsourcing human decisions and turning to risk assessment tools. So, by calling attention to discriminatory design, that is the human decision, assumptions and values that shape the process of tech development, we’re able to see the harm.”
Benjamin hopes by shedding light on the harm black communities can take back the power and pave the way for different tech development.
The battle for ethical innovation and accountability in AI is ongoing, and it’s a battle that affects us all. Ethical innovation ensures that AI systems are developed and used in a responsible and fair manner, while accountability mechanisms provide safeguards against the misuse of AI technology. Through a collective effort involving governments, industries, developers, and the public, we can create a future where AI enriches our lives while upholding the values and principles we hold dear. We all play an important role in holding AI accountable.
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