Artificial Intelligence (AI) Vs. Privacy - Striking The Perfect Balance

Author: LawSphereX

Category: Constitutional & Human Rights

Published: 2026-08-23T18:11:30.849908+00:00

Updated: 2026-08-26T17:12:05.084+00:00

ARTIFICIAL INTELLIGENCE (AI) VS. PRIVACY:

STRIKING THE PERFECT BALANCE

D Amrish Shiyamily, Christ Deemed to be University, Bengaluru

INTRODUCTION:

The rapid advancement of Artificial Intelligence (AI) has fundamentally transformed industries by enhancing efficiency, particularly in processing large datasets and making automated decisions. As AI increasingly handles personal data, concerns about privacy have risen. AI relies heavily on massive amounts of data, creating scenarios where individuals’ privacy rights may be compromised, especially given the complexities of recent legislation like the Data Protection Act, 2023 (DPA 20231). This article examines the intersection of AI and privacy, focusing on legal frameworks, landmark judgments, and the implications for AI’s societal role2. Artificial intelligence (AI) thrives on data, leveraging it to make decisions, predictions, and recommendations. This reliance on vast quantities of personal data has sparked significant concerns about privacy rights.

As AI systems become deeply embedded in everyday life, from social media to smart devices and surveillance technologies, the potential for privacy violations grows exponentially. This article explores the challenges of balancing technological advancement with the protection of individual freedoms, with a focus on regulatory frameworks, ethical dilemmas, and real-world examples. Artificial Intelligence (AI) continues to revolutionize industries, offering unprecedented opportunities and challenges. However, this transformative technology also poses significant legal risks, sparking predictions of increased AI-related litigation in 2025. This article examines key areas where disputes are expected to arise, providing a preview of the commercial litigation landscape for the year.

1 Data Protection Act, 2023 (India).

2 Acquisti, Alessandro, et al. “Privacy and Human Behavior in the Age of Information.” Science, vol. 347, no. 6221, 2015, pp. 509–514.

Artificial Intelligence (AI) has become a transformative force across multiple industries, including healthcare, finance, and retail, by providing capabilities such as data processing, task automation, and decision-making. However, these advancements come at a cost to privacy. AI systems, particularly those relying on machine learning algorithms and neural networks, often operate by analyzing large sets of personal and sensitive data, sometimes without the explicit knowledge or consent of individuals. This extensive data collection and profiling raise significant concerns, particularly about the risk of mass surveillance and data misuse. As AI continues to evolve, its ability to predict behavior, recognize faces, and even detect emotions has sparked a growing debate regarding its compatibility with existing privacy laws.

AI’s impact on privacy is complex, with its vast data usage often creating situations where individuals have little to no control over how their personal information is gathered, stored, and used. This has brought about serious privacy concerns, including unauthorized surveillance, especially in public and online space. The rapid development of Artificial Intelligence (AI) technologies has revolutionized various sectors, improving efficiency and automating complex processes. From personalized shopping experiences to healthcare diagnostics, AI's ability to process large datasets and derive predictive insights has been transformative. However, as AI continues to infiltrate industries that rely heavily on data, the right to privacy has come under increased scrutiny.

AI systems require vast amounts of personal data to function effectively, raising concerns about how these systems collect, store, and process such information. This is especially true when considering the implications of new legal frameworks like the Data Protection Act, 2023 (DPA 2023), which aim to balance technological advancements with privacy rights. In this article, we explore the intersection of AI and privacy, with a particular focus on the legal implications and protections offered by the Data Protection Act, 2023. Understanding the Right to Privacy The right to privacy is one of the most fundamental human rights, recognized under international law and enshrined in numerous national legal systems. In the Indian context, privacy has long been debated, but its legal status was solidified by the landmark case of Justice K. S. Puttaswamy (Retd. ) v.

Union of India (20173), wherein the Supreme Court of India declared privacy as a fundamental right under Article 21 of the Indian Constitution. This Justice K. S. Puttaswamy (Retd. ) v. Union of India, (2017) 10 SCC 1.

judgment marked a turning point in Indian jurisprudence, establishing that privacy is an intrinsic part of an individual's right to life and personal liberty. However, as technological advancements such as AI continue to evolve, the challenge of protecting privacy has become more complex. Privacy, in its broadest sense, refers to an individual's right to control their personal information and to be free from unwarranted surveillance, intrusion, or disclosure. It encompasses various dimensions, including informational privacy (the right to control personal data) and physical privacy (protection from physical surveillance). With the rise of digital technologies, particularly AI, personal data is increasingly collected and processed, sometimes without the explicit consent of individuals.

As AI relies on vast amounts of personal data to function, the right to privacy faces new challenges that need to be addressed through legal frameworks. AI and Its Implications for Privacy Artificial intelligence, particularly machine learning (ML) and data-driven algorithms, has the capacity to process vast quantities of data, identifying patterns, making decisions, and even predicting future outcomes. AI has numerous applications, from healthcare (where it aids in diagnosis and treatment planning) to financial services (where it assesses creditworthiness and predicts market trends). However, these technologies are not without their drawbacks, particularly when it comes to privacy. Mass Accumulation of Data: AI systems rely on large datasets to function effectively.

The data collected can include sensitive personal information, such as location data, online browsing habits, and health records. For AI to generate accurate predictions and personalized experiences, it needs to access a significant volume of personal data. This raises significant concerns about data security and the potential for misuse or unauthorized access to sensitive information. For instance, AI systems that rely on data from social media platforms may inadvertently expose an individual's private life without their consent. Profiling and Targeting: One of the key concerns with AI is its ability to create detailed profiles of individuals based on their behaviors, preferences, and interactions. These profiles can be used for targeted advertising, political profiling, and other forms of mass personalization. While this may enhance user experience in some cases, it also

leads to invasive surveillance. AI’s ability to track and analyze behavior often occurs without meaningful human oversight, making it difficult to understand how data is being used and for what purposes. This level of data aggregation increases the risk of unwanted exposure of personal information. Lack of Transparency: Many AI algorithms, particularly those that rely on machine learning, operate as "black boxes. " This means that even those who design and deploy these systems may not fully understand the reasoning behind AI-driven decisions. When decisions are made based on large datasets, the opacity of the algorithms can prevent individuals from understanding how their personal data is being processed and used. This lack of transparency creates a significant barrier to accountability and is a major privacy concern.

Individuals should have the right to know how their data is being used and the basis on which decisions affecting them are made. Surveillance: AI technologies, such as facial recognition and biometric data collection, have become increasingly prevalent in law enforcement and security. While these technologies offer benefits, such as enhanced public safety, they also pose risks to privacy. The use of facial recognition in public spaces, for example, can result in mass surveillance, where individuals are constantly monitored without their knowledge or consent. The deployment of AI-driven surveillance technologies can lead to significant intrusions into personal privacy, especially if they are not subject to proper regulatory oversight.

Data Protection Act, 2023 In response to the growing concerns over data privacy in the digital age, India enacted the Data Protection Act, 2023 (DPA 20234), which provides a comprehensive legal framework for the governance of personal data. The DPA 2023 seeks to address the challenges posed by technologies like AI by ensuring that personal data is processed in a transparent, accountable, and secure manner. The Act introduces several key provisions that aim to safeguard the privacy rights of individuals while promoting innovation and technological progress. Consent and Data Processing: One of the cornerstone provisions of the DPA 2023 is

4 Data Protection Act, 2023 (India).

the requirement for explicit and informed consent before personal data is processed. Under the Act, AI systems or any other organization that handles personal data must obtain consent that is specific to the purpose for which the data is collected. This provision aims to ensure that individuals have control over their personal information and are fully informed about how their data will be used. Data Minimization: The DPA 2023 introduces the principle of data minimization, which requires that only the minimum amount of personal data necessary to fulfill a specific purpose should be collected and processed. This provision is particularly important in the context of AI, where large volumes of data are often accumulated. By limiting data collection to what is essential, the DPA 2023 aims to reduce the risk of personal data misuse and enhance individuals' privacy protections.

Data Localization: Another significant provision of the DPA 2023 is the requirement for data localization. Certain types of personal data must be stored within India's territorial boundaries. This is particularly relevant for international corporations that use AI technologies, as it ensures that their data storage practices comply with Indian data protection laws. Data localization helps maintain national sovereignty over personal data and ensures that foreign entities are held to the same privacy standards as domestic organizations. Sanctions and Enforcement: The DPA 2023 imposes substantial penalties for noncompliance, including heavy fines for organizations that violate privacy laws. In addition, the Act establishes a Data Protection Authority (DPA) responsible for overseeing the enforcement of the Act.

The DPA has the authority to investigate complaints, issue warnings, and impose sanctions on non-compliant organizations. This regulatory framework ensures that AI systems and other entities that handle personal data are held accountable for their actions. Data Subject Rights: The DPA 2023 grants individuals several key rights regarding their personal data. These include the right to access their data, correct inaccuracies, and request the deletion of their information. By empowering individuals with these rights, the Act seeks to give people more control over how their data is used and to ensure that AI systems are held accountable for the information they process.

Landmark Decisions Influencing Privacy and AI Several landmark decisions by the Indian judiciary have played a critical role in shaping the current legal landscape regarding privacy and AI. These decisions highlight the judiciary's growing recognition of privacy as a fundamental right and its relevance in the digital age. Justice K. S. Puttaswamy (Retd. ) v. Union of India (2017)5: This case is a cornerstone in India's privacy jurisprudence. The Supreme Court's judgment affirmed that privacy is an integral part of the right to life and personal liberty under Article 21 of the Indian Constitution. The Court ruled that any encroachment upon an individual's privacy must meet a threefold test of legality, necessity, and proportionality.

This judgment has significant implications for AI systems, particularly when personal data is processed, as it emphasizes the need for legal safeguards to protect privacy . Shreya Singhal v. Union of India (20156): In this case, the Supreme Court struck down Section 66A of the Information Technology Act, which criminalized offensive online content. The Court found the provision to be vague and overbroad, violating the right to freedom of speech and expression. While the case primarily focused on free speech, it also underscored the importance of clear, precise regulations in the digital realm, especially with regard to AI-driven content moderation and data collection Anuradha Bhasin v. Union of India (2020)7: This case dealt with restrictions on internet access in Jammu and Kashmir and reinforced the idea that fundamental rights apply in virtual spaces.

The Court ruled that any restrictions on internet access must pass the test of necessity and proportionality. This decision has far-reaching implications for AI systems that rely on internet infrastructure to process personal data, highlighting the importance of protecting digital rights in an increasingly interconnected world. Impact on the Right to Privacy from Using AI Despite the advantages that AI offers, its pervasive use can significantly impact an individual's

5 Justice K.S. Puttaswamy (Retd.) v. Union of India, (2017) 10 SCC 1.

Shreya Singhal v. Union of India, (2015) 5 SCC 1. Anuradha Bhasin v. Union of India, (2020) 15 SCC 74.

right to privacy in various ways: Predictive Analytics and Behavior Tracking: AI systems are capable of tracking individual behavior and preferences across multiple platforms, leading to more accurate predictions about future behavior. While this is useful in fields like marketing and personalized services, it raises concerns about surveillance and the potential for misuse of personal information. Predictive Decisions: AI-driven decision-making is increasingly being used in various sectors, including hiring, lending, and insurance. These decisions are often made based on data, with little transparency or oversight. Individuals affected by such decisions may not understand how their data is being used or how decisions are being made, leading to potential violations of their privacy.

Biometric Data Collection: The use of biometric data, such as facial recognition and fingerprinting, has become widespread in security and law enforcement. AI systems that rely on biometric data are often deployed without individuals' consent, raising concerns about the indiscriminate collection and storage of sensitive information. Legal Protections for AI and Privacy To address these concerns, several legal protections are necessary: Algorithmic Transparency: Policymakers must mandate that AI systems provide clear explanations for their decision-making processes. This transparency would help individuals understand how their data is used and ensure that AI systems are held accountable for their actions. Regulation of Data Brokers: Data brokers that buy, sell, and trade personal information should be more strictly regulated.

These organizations must be held to the same privacy standards as other data controllers, ensuring that they comply with existing data protection laws. Ethical AI Frameworks: Governments and corporations must adopt ethical frameworks for AI development and deployment. These frameworks should prioritize

privacy, transparency, and accountability, ensuring that AI is used responsibly and respects individuals' rights. Periodic Audits: Regular audits of AI systems are essential to ensure compliance with privacy laws. These audits would help identify potential risks to privacy and ensure that organizations are taking appropriate steps to mitigate those risks. The Rise of AI-Driven Data Collection AI systems require extensive datasets to function effectively, often relying on personal data. Activities such as social media usage, online purchases, and interactions with digital platforms are analyzed by AI algorithms to enhance services, personalize recommendations, and optimize user experiences8. However, this pervasive access to personal data, frequently without explicit consent, has raised global privacy concerns.

Smart Devices and Surveillance in Homes AI-powered smart assistants like Amazon’s Alexa, Google Home, and similar devices exemplify the privacy challenges posed by AI. These devices continuously collect user information, often blurring the line between convenience and surveillance. For instance, smart assistants record conversations and track user behavior to refine their functions. In 2019, it was revealed that some tech companies used human workers to listen to recordings from these devices for quality control, often without user knowledge or consent. Such practices underscore the potential for unintentional privacy violations and highlight the ethical implications of integrating such technologies into private spaces. While these devices aim to simplify life, their widespread adoption raises critical questions about user consent and data protection.

The tension between enhancing functionality and safeguarding privacy lies at the heart of the socio-legal debate surrounding AI9. Data Protection Regulations To address privacy risks posed by AI technologies, governments worldwide have implemented regulatory measures. One of the most significant is the European Union’s General Data Protection Regulation (GDPR), introduced in 2018. The GDPR seeks to give individuals greater control over their personal data by mandating explicit consent for data 8 European Union. “Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data (General Data Protection Regulation). ” Official Journal of the European Union, 2016. 9 Cadwalladr, Carole, and Emma Graham-Harrison.

“Revealed: 50 Million Facebook Profiles Harvested for Cambridge Analytica in Major Data Breach. ” The Guardian, 17 Mar. 2018.

collection and processing and allowing users to request data deletion under specific conditions. The regulation’s transparency requirements compel AI systems to disclose how user data is utilized and processed. Facial Recognition and GDPR Challenges Facial recognition technology, one of AI’s most contentious applications, epitomizes the privacy challenges regulators face. The UK’s Information Commissioner’s Office (ICO) flagged concerns over the use of facial recognition in public spaces, particularly by law enforcement agencies10. The ICO’s findings indicated that such practices often violated GDPR principles, including transparency and data minimization. Facial recognition technology enables mass surveillance, raising questions about whether its benefits, such as enhanced security, justify its invasive nature.

The ethical debate surrounding this technology centers on its potential to normalize surveillance and infringe on individual privacy rights. AI and Corporate Data Collection Beyond government regulation, private companies’ use of AI to collect and analyze consumer data presents another layer of privacy challenges. Social media platforms, e-commerce sites, and mobile apps routinely gather behavioral data to improve user engagement and target advertisements. However, instances of overreach—such as unauthorized data sharing can lead to significant privacy violations. Cambridge Analytica Scandal A notable example of corporate misuse of AI-driven data collection is the Cambridge Analytica scandal. In 2018, it was revealed that the political consulting firm harvested personal data from millions of Facebook users without consent.

The firm used AI-powered analytics to create psychographic profiles and deliver targeted political ads during campaigns, including the 2016 U. S. presidential election. This case highlighted AI’s potential to manipulate public opinion and its implications for democratic processes and individual privacy. Ethical Design and Privacy Safeguards As the use of AI expands, addressing privacy concerns requires a shift toward ethical design principles. Concepts such as “privacy by design” advocate embedding privacy safeguards into AI systems from their inception. Techniques like differential privacy, which allows AI to analyze data without revealing specific information, 10 Vincent, James. “UK Police’s Use of Facial Recognition Violated Human Rights, Court Rules. ” The Verge, 11 Aug. 2020.

and federated learning, which enables AI training on decentralized data, offer promising solutions to mitigate privacy risks11. The Future of Privacy in an AI-Driven World While regulations like the GDPR represent significant progress, the rapid pace of AI development often outstrips legal frameworks. Establishing global data protection standards tailored to AI’s unique challenges is essential. Collaborative efforts among governments, tech companies, and civil society can ensure that privacy rights are upheld while fostering innovation. AI and Copyright Disputes At least two dozen pending cases involve copyright owners alleging unauthorized use of their works to train large language models. This trend highlights the tension between technological innovation and intellectual property rights12.

For instance, prominent AI developers face lawsuits for using copyrighted material without permission, raising critical questions about fair use and data ownership. AI Bias and Employment Discrimination AI tools used for screening job applicants have drawn scrutiny for perpetuating biases. Claims may arise under civil rights laws, challenging AI-driven hiring practices that disadvantage protected groups. Legal experts anticipate heightened focus on algorithmic accountability to ensure compliance with anti-discrimination laws. Antitrust and AI Pricing Tools The Department of Justice (DOJ) and state attorneys general recently sued RealPage, a property management software firm, alleging its AI-trained pricing algorithms facilitated landlord collusion, inflating rental prices. While RealPage denies the allegations, the case underscores how AI tools can inadvertently breach antitrust laws.

Legal observers expect similar cases as companies increasingly rely on AI for pricing strategies. 11 Cornerstone Research and Stanford Law School. "Securities Class Action Clearinghouse Mid-Year Report. " July 2024.

12 Chiu, David. "San Francisco City Attorney Lawsuit Against Deepfake Websites." August 2024.

Securities Litigation AI-related claims in securities litigation are on the rise, with allegations of failing to disclose material facts about the use of AI technology. According to Cornerstone Research and the Stanford Law School Securities Class Action Clearinghouse, six AI-related securities class actions have already been filed. These cases highlight the need for transparency and compliance in corporate AI initiatives. Product Liability and AI Malfunctions AI product liability claims are emerging as a critical area of litigation. Hypothetical scenarios include AI navigation errors in airplanes or database analysis failures causing significant harm13. For instance, a Florida mother sued chatbot startup Character. AI, alleging its technology contributed to her son’s suicide. Such cases illustrate the potential for tort-based claims involving AI-induced injuries.

Deepfake Technology and Privacy Violations AI-generated deepfake pornography has sparked legal action, including a groundbreaking lawsuit filed by San Francisco City Attorney David Chiu against websites distributing nonconsensual nude images. Privacy, fraud, and consumer protection-related claims are expected to expand as deepfake technology becomes more pervasive. Federal Regulation of AI Under the incoming Trump administration, regulatory enforcement may shift dramatically. Legal practitioners anticipate a “giant step back in enforcement” compared to the Biden administration, particularly in areas like AI, healthcare, and fintech. This shift could lead to more litigation as companies navigate a less predictable regulatory environment14. Supreme Court Decisions and Administrative Law Two recent Supreme Court decisions Loper Bright Enterprises v. Raimondo and Corner Post, 13 Tene, O.

, & Polonetsky, J. “Big Data for All: Privacy and User Control in the Age of Analytics,” Northwestern Journal of Technology and Intellectual Property, vol. 11, no. 5, 2013, pp. 239-273. 14 Angwin, S. , Parris, J. , & Mattu, N. “The Unregulated World of California’s Consumer Privacy Act,” The Verge (2019).

Inc. v. Board of Governors of the Federal Reserve System 15have reshaped administrative law. The overturning of the “Chevron doctrine” and changes to the statute of limitations for challenging agency rules could prompt a wave of lawsuits against regulatory agencies. Companies may seize this opportunity to contest longstanding regulations. The potential privacy risks associated with AI are not limited to surveillance and data collection; they extend to issues of discrimination and bias. AI systems, while designed to be objective, can perpetuate existing societal biases due to the data they are trained on. Studies have shown that AI algorithms can sort individuals based on discriminatory characteristics such as race, gender, or socioeconomic status, which in turn can lead to biased outcomes in areas like hiring, lending, and law enforcement .

For instance, predictive policing tools, which utilize AI to forecast criminal activities based on historical data, risk reinforcing biased law enforcement practices and disproportionately affecting marginalized communities. These concerns emphasize the urgent need for AI technologies to undergo thorough scrutiny, ensuring they do not perpetuate harmful stereotypes or produce unfair results. While privacy laws such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States offer some safeguards for individuals, they fall short of addressing the complexities introduced by AI16. The GDPR, considered one of the most stringent privacy regulations globally, provides guidelines on data collection, processing, and the rights of individuals, such as the right to erasure and access to data.

However, its provisions are not specifically tailored to the unique risks posed by AI. For example, the regulation does not adequately address automated decisionmaking, a fundamental aspect of many AI systems. The absence of clear rules regarding AI’s impact on decision-making processes makes it difficult to ensure that individuals are fully informed about how their data is being used and to what extent AI influences decisions about them. In addition, the cross-border nature of AI operations introduces significant challenges for privacy enforcement. AI systems typically operate across multiple jurisdictions, with data being transferred internationally, creating potential conflicts between privacy laws that vary widely across countries. The GDPR has attempted to address this by setting guidelines for

15 Loper Bright Enterprises v. Raimondo, 598 U.S. (2024).

16 European Sources. “Proposal for a Regulation Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act),” 2024.

international data transfers, but issues remain around how these laws can be enforced when AI systems are not confined to specific national borders. This lack of consistency and coordination between national regulations complicates the implementation of effective privacy protections. Moreover, existing privacy regulations do not adequately address the ethical concerns associated with AI, such as the accountability of corporations deploying these technologies. One of the key challenges in regulating AI is the "black box" nature of many AI systems, which makes it difficult to understand how decisions are made. This opacity raises concerns about the lack of accountability when AI systems cause harm, particularly in sensitive areas like healthcare, criminal justice, or finance 17.

For example, if an AI system makes an erroneous decision, such as denying a loan or providing inaccurate medical advice, it can be challenging to determine who should be held responsible , the developer, the company that deployed the system, or the AI itself. As AI systems become more autonomous, the need for clear legal frameworks that specify who is liable for AI-driven decisions becomes more pressing. To address these gaps, there is a growing consensus among legal scholars and policymakers that AI-specific regulations are necessary. The European Union is currently working on the AI Act, which seeks to regulate high-risk AI systems, ensuring that they adhere to strict guidelines for transparency, accountability, and safety . Such laws are aimed at ensuring that AI systems are designed with privacy and security in mind, promoting what is known as "privacy by design.

" This approach ensures that privacy considerations are embedded into the design process of AI technologies from the outset, rather than being added as an afterthought. Additionally, new privacy regulations must focus on providing individuals with greater control over how their data is collected and used by AI systems. Enhancing consent mechanisms and offering clearer options for data deletion and modification are essential for empowering individuals and protecting their rights . Finally, one of the most critical aspects of addressing AI’s impact on privacy is ensuring human oversight in AI decision-making processes. In sectors like healthcare, criminal justice, and employment, AI decisions can have profound consequences for individuals’ lives.

Therefore, it is essential to establish mechanisms that allow for human intervention, particularly when AI systems are involved in making critical decisions. Legal frameworks must ensure that these 17 Kuner, Christopher. “The Law of Data Privacy in the European Union. ” Fordham International Law Journal, vol. 39, no. 4, 2016, pp. 1-40.

systems are not fully autonomous, thereby preserving human judgment and accountability in decisions that affect people's rights and freedoms 18.

CONCLUSION:

AI offers transformative potential but poses substantial privacy risks. Achieving a balance between technological innovation and the protection of individual rights is paramount. By strengthening regulatory frameworks, promoting ethical AI design, and enhancing transparency in data practices, societies can harness AI’s benefits without compromising fundamental freedoms. AI to be a pivotal period for AI-related litigation. As legal frameworks struggle to keep pace with technological advancements, courts will play a crucial role in defining the boundaries of AI use. From intellectual property disputes to antitrust claims, the evolving landscape will challenge litigators and policymakers alike19.

Ensuring responsible AI development and deployment will require a delicate balance between innovation and accountability In conclusion, while AI presents significant opportunities for innovation, it also poses serious privacy risks that current legal frameworks are ill-equipped to address. Laws such as the GDPR and CCPA offer some protections, but they must be updated to account for the complexities introduced by AI. There is an urgent need for AI-specific legislation that promotes transparency, accountability, and ethical use of AI technologies while safeguarding individuals' privacy rights. As AI continues to evolve, it is crucial for governments, industries, and international bodies to work together to create a regulatory landscape that balances the benefits of AI with the need to protect individual privacy. 18 Cowgill, R. , Dellavigna, S. , & Leclercq, M.

“Bias and Productivity in Humans and Machines,” American Economic Review, Vol. 110, No. 2, 2020, at 6. 19 Calo, Ryan. “Artificial Intelligence Policy: Risks and Opportunities,” Harvard Journal of Law & Technology, vol. 29, no. 2, 2016, pp. 121-145.