Podcasts > Civics 101 > How Should We Govern the Algorithm?

How Should We Govern the Algorithm?

By NHPR

Dive into the ethical conundrum of artificial intelligence's role in governance with "Civics 101," guided by hosts Hannah McCarthy and Nick Capodice alongside guest expert Aziz Huq. This episode delves into the contentious realm of law enforcement's reliance on AI tools, such as facial recognition, which despite its advancement, raises alarms about civil rights violations and the perpetuation of inherent biases. A particularly striking case study from the NYPD and an in-depth Georgetown University report strikes at the heart of these concerns, unmasking the dangers of wrongful identification and the potential for misuse within the policing system.

"Civics 101" also sheds light on the uneven terrain of AI regulation and consumer data privacy across the United States, revealing a stark dissonance between state-level initiatives and a glaring absence of federal legislation. The episode juxtaposes AI's prowess in churning out present trends from vast data against privacy incursions, as illustrated by the controversy surrounding Target's predictive algorithms. This leads to a discussion of the Biden administration's executive order on AI, which aims to establish guiding principles for AI policy that champion safety, fairness, and innovation, while contending with the specter of algorithmic discrimination and upholding the sanctity of civil liberties.

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How Should We Govern the Algorithm?

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How Should We Govern the Algorithm?

1-Page Summary

Controversial government use of AI

Law enforcement agencies' use of artificial intelligence, especially facial recognition software, has been met with substantial critique from legal experts and civil rights advocates. Aziz Huq, a notable law professor, addresses the implications of these tools for civil liberties and the inherent biases they may perpetuate. In one case, the NYPD's utilization of facial recognition led to an arrest through the dubious method of comparing a pixelated image to that of a celebrity, evidencing both the inaccuracy and the risks of wrongful identification. A Georgetown University report underscores the technology's unreliability and potential misuse, signifying profound concerns about its current implementation in policing.

Lack of laws governing AI and data privacy

There's a pronounced incongruity between state and federal laws on AI and data privacy. The federal government lags behind, lacking comprehensive legislation for consumer data protection, while some states have taken the initiative with their own robust data privacy laws. With only 12 states having passed such laws, the United States presents a fragmented legal landscape that reveals a critical gap at the national level to secure consumer data and regulate AI.

AI predictions of current facts vs future events

Artificial intelligence is adept at drawing out current realities from existing data, yet this often intrudes into the personal arena and sparks data privacy issues. Target's algorithm, which predicted pregnancy among customers using their buying habits, exemplifies the tension between predictive analytics and privacy. Even though Target modified their advertisement methods to camouflage the targeted nature of their marketing, the incident spotlights the profound privacy concerns that come with AI's predictive capabilities.

Biden's executive order on guiding principles for AI policy

The Biden administration has responded to the concerns over AI with an executive order setting forth principles for AI governance. This order underscores the importance of safety, equity, privacy, and innovation, highlighting the administration’s commitment to fighting algorithmic discrimination and ensuring AI does not further injustices. It stresses the protection of consumers from AI-related fraud and biases and calls for lawful data use, while mandating the appropriate training of personnel to oversee the safe and transparent deployment of AI technologies, with a focus on mitigating bias and upholding civil liberties.

1-Page Summary

Additional Materials

Clarifications

  • Aziz Huq is a prominent law professor known for his expertise in constitutional law and civil liberties. He has extensively researched and written about the legal implications of emerging technologies, including artificial intelligence and its impact on civil rights. Huq's work often focuses on the intersection of law, technology, and individual freedoms, providing valuable insights into the legal challenges posed by new technologies in society. His analysis and commentary on issues such as government surveillance, privacy rights, and algorithmic bias have contributed significantly to the ongoing discourse on technology regulation and civil liberties.

Counterarguments

  • Law enforcement's use of AI, including facial recognition, can enhance public safety and efficiency if implemented with proper oversight and regulation.
  • Facial recognition technology is continuously improving, and when used responsibly, it can be a valuable tool for identifying suspects and solving crimes.
  • The potential misuse of AI in policing could be mitigated through transparent policies, accountability measures, and ongoing audits to ensure ethical use.
  • A national approach to AI and data privacy legislation could potentially stifle innovation and impose one-size-fits-all regulations that may not be suitable for all states.
  • States pioneering their own data privacy laws can serve as laboratories for democracy, allowing for diverse approaches that could inform better federal legislation in the future.
  • AI's predictive capabilities can lead to more personalized services and benefits for consumers, provided that privacy is respected and data is used ethically.
  • Target's response to privacy concerns demonstrates that companies can adapt their practices to address consumer privacy while still leveraging AI for business insights.
  • The principles set forth in Biden's executive order may be too broad or vague, potentially leading to challenges in implementation and enforcement.
  • The executive order could impose administrative burdens on agencies and businesses that may hinder AI innovation and development in the United States.
  • Training personnel for AI oversight is a positive step, but it may not be sufficient without a clear legal framework and standards for measuring the success of such training programs.

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How Should We Govern the Algorithm?

Controversial government use of AI

The Board of Police Commissioners and law experts like Aziz Huq provide critical views on the use of artificial intelligence tools, particularly facial recognition software, by law enforcement agencies, highlighting significant concerns over its accuracy, potential bias, and threat to civil liberties.

Law enforcement use of facial recognition software

Concerns over accuracy, bias, and civil liberties

Aziz Huq, a law professor, discusses his work involving a machine learning tool used in Chicago, known as a strategic subjects list. This tool compiled data on welfare and criminal behavior to predict who the police should stop, a practice that reminds of "stop and frisk" and that disproportionately targets black and Latino communities. Huq's interest in AI arose from observing how it is used by the government and the implications for civil liberties and potential biases.

In one example, NYPD detectives used facial recognition software to identify a suspect accused of stealing beer from a CVS. To improve on the pixelated image from the security footage, they employed a photograph of actor Woody Harrelson, believing there was a resemblance to the suspect. The facial recognition search produced an arrest based on matching the suspect to Harrelson's photo; however, this approach also led to incorrect matches, raising serious accuracy concerns.

Georgetown University released a report acknowledging the role o ...

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Controversial government use of AI

Additional Materials

Clarifications

  • The strategic subjects list in Chicago is a controversial tool that uses data on welfare and criminal behavior to predict individuals who law enforcement should focus on. This practice has raised concerns about its resemblance to discriminatory policing methods like "stop and frisk" and its disproportionate impact on black and Latino communities. The list is part of the broader discussion on the use of artificial intelligence tools in law enforcement and the potential implications for civil liberties and biases. The tool's implementation and impact have sparked debates on the ethical and legal considerations surrounding predictive policing practices.
  • "Stop and frisk" is a policing tactic where officers detain, question, and search individuals on the street for weapons or contraband. It has been criticized for disproportionately targeting minority communities and raising concerns about racial profiling and civil rights violations. The practice involves stopping individuals based on suspicion rather than evidence of wrongdoing, leading to debates about its effectiveness and impact on community trust. Critics argue that "stop and frisk" can lead to harassment, discrimination, and the violation of Fourth Amendment rights against unreasonable searches and seizures.
  • The use of AI by t ...

Counterarguments

  • Facial recognition technology can significantly enhance public safety by identifying suspects more quickly and accurately than manual methods.
  • The technology is constantly improving, with advancements in AI reducing the rate of false positives and increasing overall accuracy.
  • Bias in facial recognition can be mitigated through better training data, more diverse datasets, and regular audits to ensure the software performs equitably across different demographics.
  • Law enforcement agencies can implement strict guidelines and oversight to prevent misuse of facial recognition technology and protect civil liberties.
  • The use of facial recognition software can be regulated to ensure transparency and accountability, with clear policies on when and how it can be used.
  • Facial recognition can be a valuable tool in non-law enforcement contexts, such as finding missing persons or identifying victims of human trafficking.
  • The strategic subjects list and similar tools, if used responsibly and with proper oversight, could potentially help in preemptive crime prevention and resource allocation for community support services.
  • The concerns about civil liberties should be balanced with the potential benefits of using AI in law enforcement, such as reducing ...

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How Should We Govern the Algorithm?

Lack of laws governing AI and data privacy

The disparity between state and federal laws regarding AI and data privacy is clear: while the federal government has yet to implement comprehensive privacy laws specifically targeting consumer data, states are leading the charge in protecting this information.

Differences in state vs. federal laws

Consumer data protection in the United States is currently a patchwork system, primarily regulated at the state level due to a lack of overarching federal legislation. Only 12 states have passed compr ...

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Lack of laws governing AI and data privacy

Additional Materials

Clarifications

  • The lack of laws governing AI and data privacy indicates a gap in regulations that specifically address how artificial intelligence technologies handle and protect personal information. This absence of comprehensive legislation at both the state and federal levels has created a fragmented system where different regions may have varying levels of protection for consumer data. The evolving landscape of AI and data usage has outpaced the development of clear legal frameworks, leading to uncertainties and inconsistencies in how privacy is managed in the digital age. Addressing these gaps is crucial to ensure consistent and robust protection for individuals' data in an increasingly data-driven society.
  • The disparity between state and federal laws regarding AI and data privacy in the United States highlights the differences in regulations set by individual states compared to the overarching laws established at the federal level. This discrepancy means that while some states have their own comprehensive data protection laws, there is no unified federal legislation that covers all aspects of data privacy and AI regulation. This lack of uniformity can lead to inconsistencies and challenges in ensuring a cohesive approach to protecting consumer data and regulating AI technologies across the country.
  • The term "patchwork system of consumer data protection in the United States" refers to the fragmented and inconsistent nature of data privacy regulations across different states, leading to a lack of uniformity in how consumer data is protected. This means that each state may have its own set of rules and regulations governing data privacy, creating a complex and varied landscape for businesses and consumers to navigate. The absence of a cohesive federal framework results in a decentralized approach to data protection, with states independently enacting their own laws to address privacy concerns. This decentralized system can pose challenges for businesses operating across multiple states, as they must comply with a diverse range of regulations that may differ significantly from one another.
  • The legislative approach to privacy across the country refers to how laws and regulations related to privacy and data protection are developed and enforced at both the state and federal levels in the United States. This includes examining the differences in approaches taken by individual states compared to the ...

Counterarguments

  • The federal government may be intentionally allowing states to experiment with data protection laws to see what works best before enacting a federal standard.
  • A single federal law might not adequately address the unique needs and concerns of individual states, which can be more effectively managed through state-specific legislation.
  • Federal legislation can sometimes be slow to adapt, and state laws may offer more agility to respond to the rapidly evolving technology and data privacy landscape.
  • Comprehensive federal privacy laws could potentially stifle innovation by imposing one-size-fits-all regulations that may not be suitable for all types of businesses or technologies.
  • The current state-led approach allows for a diversity of legal frameworks, which can provide valuable insights into the advantages and disadvantages of different regulatory strategies.
  • The absence of feder ...

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How Should We Govern the Algorithm?

AI predictions of current facts vs future events

AI has the capability of deriving current facts from data but raises several privacy concerns, as evidenced by Target's use of predictive algorithms.

Target predicting which customers are currently pregnant based on purchasing data

Target implemented a predictive algorithm that analyzed customer purchasing data to identify which customers were likely pregnant. By observing purchasing trends such as the buying of unscented lotion and large purses, Target could send coupons for prenatal vitamins and related products to those customers.

This practice, however, raised privacy concerns about revealing sensitive information. It made some people uncomfortable by showcasing the ability of companies to infer and act upon highly personal l ...

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AI predictions of current facts vs future events

Additional Materials

Clarifications

  • The predictive algorithm used by Target analyzed customer purchasing data, looking for patterns like the buying of unscented lotion and large purses, to identify potential pregnant customers. This data analysis allowed Target to send targeted coupons for pregnancy-related products to these customers. Target adjusted its strategy by blending pregnancy-related ads with unrelated items to make the targeted marketing less obvious.
  • The connection between buying unscented lotion and large purses with predicting pregnancy lies in the analysis of consumer behavior data. Retailers like Target have found that certain purchasing patterns, like the simultaneous purchase of unscented lotion and large purses, can indicate a high likelihood of a customer being in the early stages of pregnancy. This insight allows companies to target specific products and promotions to customers who may be expecting a child, based on these subtle but significant buying patterns.
  • Target adjusted their strategy by blending their targeted ads for pregnancy products with unrelated items, such as wine glasses beside cribs in mailers. This blending helped disguise the focused nature of their marketing and made it less obvious that they were targeting pregnant women.
  • Blendin ...

Counterarguments

  • AI's ability to derive current facts from data can be seen as a tool for efficiency and personalization rather than a privacy concern if handled with transparency and user consent.
  • The effectiveness of predictive algorithms like Target's in identifying pregnant customers may not be universally high, as they could lead to false positives and negatives, affecting customer experience.
  • There may be ethical ways to use purchasing data for predictions if customers are clearly informed and given a choice to opt-in or opt-out of such analysis.
  • Target's strategy to blend targeted ads with unrelated items could be viewed as deceptive, potentially eroding trust between the company and its customers.
  • The concerns about privacy may be mitigated if companies like Target implement robust data protection measures and adhere to strict data privacy regulations.
  • The use of predictive analytics for marketing ...

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How Should We Govern the Algorithm?

Biden's executive order on guiding principles for AI policy

President Biden's latest executive order focuses on establishing guiding principles for the governance of artificial intelligence (AI) within the federal government's realm of influence.

Safety, equity, privacy, and innovation

The core of Biden’s executive order revolves around four fundamental pillars of safety, equity, privacy, and innovation.

Orders study of algorithmic discrimination and steps to mitigate bias

The executive order emphasizes that AI policies must be dedicated to equity and civil rights, ensuring that AI does not perpetrate or exacerbate denials of equal opportunity and justice. Consumers interacting with AI are to be safeguarded against fraud, bias, discrimination, and violations of their privacy. Additionally, it highlights the necessity of lawful data collection, ensuring the protection of privacy and civil liberties despite ...

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Biden's executive order on guiding principles for AI policy

Additional Materials

Clarifications

  • Algorithmic discrimination occurs when artificial intelligence systems exhibit bias or unfairness in their decision-making processes, leading to unequal treatment of individuals based on factors like race, gender, or other protected characteristics. This bias can result from flawed data inputs, inadequate algorithm design, or historical societal inequalities embedded in the data used for training AI systems. Efforts to mitigate algorithmic discrimination involve implementing measures to ensure fairness, transparency, and accountability in AI technologies to prevent harm and uphold ethical standards. Addressing algorithmic discrimination is crucial for promoting equity, protecting civil rights, and fostering trust in AI applications across various sectors.
  • Civil liberties encompass fundamental rights and freedoms that governments are obligated to protect, such as freedom of speech, privacy, and due process. They serve as a shield against government overreach and ensure individuals' rights are upheld. These liberties are crucial for maintaining a fair and just society, where individuals are free to express themselves and live without unwarranted interference. The protection of civil liberties is essential for safeguarding democracy and promoting equality ...

Counterarguments

  • The executive order may be overly ambitious, as the principles of safety, equity, privacy, and innovation can sometimes be in conflict, and balancing them effectively in practice can be challenging.
  • There may be concerns about the feasibility of completely eliminating bias and discrimination in AI, as these technologies often reflect the biases present in society and the data they are trained on.
  • The focus on lawful data collection might not be sufficient to address all privacy concerns, as the definition of what is lawful can change and may not always keep pace with technological advancements or public expectations.
  • Ensuring proper hiring and training of personnel is a positive step, but it may not be enough to guarantee the safety and comprehensibility of AI systems, especially as they become more complex.
  • The order's emphasis on federal government AI policies might not adequately address the use and regulation of AI in the private sector, where much of the innovation and application of AI technologies occur.
  • The a ...

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