PDF Summary:The Hank Show, by McKenzie Funk

In The Hank Show, McKenzie Funk delves into the remarkable career of Hank Asher, a data fusion pioneer whose technological innovations transformed surveillance and data analysis. Asher's journey takes us from his house painting days to developing groundbreaking systems like AutoTrack, Accurint, and MATRIX that integrated vast troves of personal data.

The book explores how Asher's post-9/11 counterterrorism tools raised privacy concerns and enabled civil liberties erosion. It examines the rise of social media data, predictive policing algorithms, and alternative information sources that exacerbated systemic biases. Funk contemplates the ethical implications of data-driven technologies and their influence on societal control.

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Practical Tips

• Engage in online simulations or games that involve strategy and the use of intelligence to make decisions. Look for games that require you to gather information from multiple sources and make strategic decisions based on that information. This will help you develop an intuitive understanding of how data fusion can influence outcomes in complex scenarios.
• Develop a habit of staying informed about data privacy laws and practices by subscribing to newsletters or podcasts focused on cybersecurity. This will help you understand the current landscape of data protection and the importance of databases like MATRIX in counterterrorism efforts. Use this knowledge to make more informed decisions about sharing your personal information online and to advocate for better data protection measures in your community.
• You can enhance your personal security by regularly checking various public databases to ensure your information is accurate and hasn't been compromised. For instance, periodically review your property records, court records, and other public information to spot any discrepancies or unauthorized changes that could indicate identity theft or fraud.
• Volunteer with local emergency response or community watch programs to contribute to and learn from collective security efforts. Your participation can provide you with insights into the broader security network and how information is gathered and used, akin to the workings of a fusion center.
• Develop a personal network of contacts from different fields. Reach out to acquaintances in various professions such as healthcare, education, law enforcement, and local government to build a diverse contact list. By doing so, you create your own informal fusion center, allowing you to access a broad range of insights and advice when facing complex personal or community issues.
• Improve your online research skills by learning advanced search techniques used in professional data tools like TLOxp. Take an online course or watch tutorials on Boolean search logic, which can help you conduct more effective internet searches. This skill will allow you to find more relevant information quickly, whether you're researching a new topic of interest, vetting a potential contractor, or looking into a second-hand car purchase.
• Explore the requirements for government contracting to understand what agencies like the CIA and NSA look for in potential partners. Start by visiting the official websites of these agencies, where they often list their procurement needs and guidelines for contractors. This will give you a sense of the standards and expectations for security, confidentiality, and technical capabilities.
• You can leverage publicly available datasets to enhance your understanding of data analysis. Start by exploring government databases, such as data.gov or the European Union Open Data Portal, where you can download datasets on various topics. Use free tools like Google Sheets or Microsoft Excel to practice sorting, filtering, and visualizing data, which will help you develop a foundational skill set in data handling.

Asher's Surveillance Systems' Privacy and Civil Liberties Concerns

Funk, throughout the book, underscores the lack of sufficient oversight and regulation when it came to how Asher's private information was used, contributing to a growing erosion of privacy and an environment where data breaches and identity theft could flourish. This section details the limitations of self-regulation within the data-broker industry, the inadequacy of the FTC and congressional investigations into ChoicePoint and LexisNexis, along with the ongoing concerns about the abuse of Asher's technologies by law enforcement and the intelligence community.

Insufficient Oversight and Regulation of Asher's Personal Data Use

The author criticizes the prevailing trust in self-regulation within the data brokerage field and the insufficient oversight of how companies like Asher's use individuals' data. Despite the early concerns about privacy and potential for misuse, agencies readily adopted his products, placing unwavering faith in the machine's accuracy while overlooking inherent biases and the flawed logic of combining data. Funk points to the ineffectiveness of the FTC and congressional investigations following breaches at companies like LexisNexis and ChoicePoint as evidence that the government was unable—or unwilling—to hold data brokers accountable. He argues that relying on self-imposed guidelines, like those put forward by IRSG, proved inadequate in preventing widespread abuse and exploitation of people's data.

Practical Tips

• You can enhance your digital privacy by using browser extensions that block trackers and cookies. These tools prevent companies from collecting your browsing data without your consent. For example, installing an extension like Privacy Badger or uBlock Origin can automatically detect and block tracking scripts on websites you visit, reducing the amount of data companies can gather about you.
• You can evaluate the trade-offs between convenience and privacy by creating a personal privacy audit. Start by listing all the devices and services you use that may collect personal data. For each item, research and note down what data is collected and how it's used. Then, decide if the benefits you receive from the product outweigh the privacy you're giving up. This will help you make informed decisions about which technologies to continue using.
• Create a "trust but verify" habit by setting a monthly reminder to review and research one piece of technology you regularly use. Look for articles, user reviews, and alternative viewpoints to assess its reliability and uncover any biases it might have. If you rely on a fitness tracker, take time to compare its data with manual tracking to see if there's a discrepancy.
• Start using cash or prepaid cards for certain transactions to minimize the digital trail you leave behind. Data brokers often collect information from credit card transactions, so by using cash or prepaid cards, especially for sensitive purchases, you reduce the amount of data available to them.
• You can audit your personal data sharing habits by keeping a "Data Diary" for one week, noting every time you provide personal information online. This could include signing up for newsletters, entering information on shopping sites, or even interacting with social media platforms. After a week, review your diary to identify which data you've shared and consider if it was necessary. This awareness can lead to more mindful data-sharing practices.

Data Breaches and Identity Theft From Asher's Companies

This section details how the shortcomings of protective measures and the intrinsic vulnerabilities of systems built for mass data collection made Asher's companies prime targets for data breaches and identity theft. Despite his insistence that his data would be used for legitimate purposes, ChoicePoint and LexisNexis—by then housing billions of records and trillions of data points—experienced significant breaches in the mid-2000s, exposing the personal information of hundreds of thousands of Americans to identity thieves. Funk attributes these vulnerabilities to inadequate technical safeguards and a fundamental failure to understand how powerful—and how fragile—these systems actually were. He underscores the parallel between stealing from banks and data brokerage companies: "Why do people steal from banks? Since the value lies there. Why steal from a data broker? Since the money's there." According to Funk, this was inevitable due to the growing data-based economy.

Practical Tips

• You can enhance your digital hygiene by creating unique email addresses for different types of accounts. For instance, use one email for social media, another for financial services, and a third for shopping sites. This way, if one account is compromised, it doesn't put all your other accounts at risk.

Other Perspectives

• The focus on Asher's companies might overshadow the broader issue that the entire industry faces similar risks and that data breaches are a widespread challenge, not unique to these companies.
• The breaches might have been a result of human error or social engineering, which no amount of technical safeguards can completely prevent.
• Systems designed for mass data collection can incorporate robust security measures that mitigate intrinsic vulnerabilities.
• The rapid evolution of technology can outpace security measures, meaning that what was once considered adequate may quickly become obsolete due to new threats and vulnerabilities.
• The companies might have been aware of the risks but accepted them as part of doing business in a rapidly evolving technological landscape, where complete security is not always achievable.
• The regulatory environment for banks is often more stringent and well-established compared to the emerging and rapidly evolving data industry, which may not yet have equivalent safeguards and oversight in place.
• It's possible to argue that the value of data is not the only reason it's targeted; sometimes, data is stolen to cause harm to an individual or organization rather than for direct financial gain.
• The framing of data breaches as inevitable might deflect from the accountability of companies that fail to implement robust security practices.

Surveillance Transformation With Social Media and Information Integration/Brokerage

This section examines the integration of Facebook, Spokeo, and additional social media platforms within the data fusion ecosystem, highlighting how these companies not only became an additional valuable source of personal information for traditional brokers but also a powerful new avenue for targeted marketing, advertising, and even voter manipulation.

Social Media Like Facebook as Sources of Individual Information

Funk examines the rise of Facebook and other social media platforms, demonstrating how the shift from traditional data brokering—piecing together identities from scattered public records—to social-media-driven data collection fundamentally altered the dynamics of surveillance. This section details how platforms such as Facebook, LinkedIn, and others entice users to inadvertently build their own dossiers via likes, friend lists, shared photos, and online interactions, creating a valuable new source of data for brokers seeking to create comprehensive profiles on individuals.

User Profile and Social Graph Data Harvesting by Companies Like Cambridge Analytica

The author reveals how this rich trove of data from social networks was quickly exploited by companies like Cambridge Analytica, who sought to leverage its predictive power to influence hearts and minds and even sway election outcomes. This case study, central to the scandal involving Cambridge Analytica, shows how quiz apps like "thisisyourdigitallife" and "myPersonality" could be used to surreptitiously siphon user profiles—and their friends' profiles—transferring vast amounts of information, including personal preferences and psychological profiles, to third parties for analysis and strategic messaging. Funk highlights how this exploitation of privacy, enabled by Facebook's open platform and lenient data-distribution policies, allowed political campaigns and other actors to engage in unprecedented levels of manipulation, pushing the boundaries of traditional direct marketing and ushering in a new era of “data-driven propaganda.”

Other Perspectives

• While companies like Cambridge Analytica did exploit social network data, it's important to recognize that the predictive power of such data is not absolute and can sometimes lead to inaccurate or unreliable profiling.
• The platforms that host such quiz apps also have systems in place to protect user data, and the misuse by specific apps should not overshadow the efforts made by the platforms to secure user privacy.
• Strategic messaging based on data analysis can enhance user experience by providing more personalized and relevant content.
• Facebook has since taken significant steps to tighten its data policies and restrict third-party access to user information.
• Some argue that the public has a degree of agency and critical thinking skills that can mitigate the impact of targeted political messaging, suggesting that voters are not as easily manipulated as implied.
• The term "data-driven propaganda" could be considered loaded, as data analysis for audience targeting is a common practice across many industries, not just politics.

Integrating Social Media Into Asher's System for Fusing Data

Highlighting the evolving nature of Asher's pursuit of information, the author details his efforts to incorporate social media data into his own data fusion platforms. This drive to stay ahead of the curve, characteristic of Asher's entrepreneurial style, pushed him to expand his company's capabilities beyond official documents and financial headers. Acknowledging the massive data harvesting potential of Facebook, he sought to acquire startups like Spokeo that had developed expertise in extracting profiles and additional social media information. Though ultimately unsuccessful in securing these acquisitions, Asher's efforts demonstrate his recognition that social media would be an essential battleground for information brokers and a vital element of future surveillance systems—a prediction that proved eerily accurate.

Practical Tips

• Develop a better understanding of your network's interests by creating a simple survey that you can share on your social media channels. Ask questions related to the topics you usually post about to see what your followers are most interested in. Use the responses to guide your future posts, ensuring they resonate more with your audience's preferences.
• Collaborate with non-competing businesses to offer bundled services or products. If you own a bookstore, partner with a local art supply store to create reading-and-art kits for hobbyists. This strategy can attract a new customer base and provide more value to existing customers.
• Consider collaborating with local universities or tech incubators to gain insights into social media data extraction. Many of these institutions run projects or workshops with a focus on data science and could provide practical knowledge or partnership opportunities without the need for a full acquisition.

Data Brokerage Industry Growth and Persistent Identifier Proliferation

This section discusses the explosive growth and consolidation of the data brokerage industry, fueled by the proliferation of new data sources, the increasing demand for risk scoring and predictive analytics, and the development of "alternative data" techniques that extended far beyond traditional credit scoring methods.

Alternative Information and Predictive Analytics in Finance/Insurance Industries

Funk describes how the data brokerage industry expanded its reach into industries like finance and insurance, moving beyond simply providing information to offering predictive analytics services driven by unconventional data. Driven by the principle that any information can be used for credit analysis, companies began using machine learning to analyze a wide array of non-traditional data points—from web browsing history and social media activity to vehicle ownership and even typos in loan applications—to assess everything from how likely someone is to default on a loan to their risk of hospital readmission. This insatiable hunger for information, the author explains, effectively turned our digital exhaust into an asset for data vendors and a disadvantage for individuals, entrenching a system where our past choices could have an outsized impact on future opportunities.

Context

• Machine learning algorithms can identify patterns and correlations in large datasets that might not be apparent through traditional analysis. This allows for more nuanced risk assessments and personalized product offerings.
• Advances in data processing and storage technologies have made it feasible to analyze vast amounts of unconventional data quickly and cost-effectively.
• While these methods can lead to more accurate credit assessments, they can also result in individuals being unfairly penalized for behaviors that are not directly related to creditworthiness.
• Errors in applications can be seen as indicators of carelessness or lack of attention to detail, which some algorithms might correlate with higher risk behavior. This data point is used to assess the reliability and conscientiousness of an applicant.
• Non-traditional data sources, such as social media activity or web browsing history, provide additional insights into a person's behavior and lifestyle, which can be indicative of their financial reliability or health risks.
• Digital exhaust has become a valuable commodity in the digital economy. Companies can monetize this data by selling it to third parties or using it to enhance their own services, creating a significant revenue stream.
• The reliance on historical data can hinder economic mobility, as individuals with less favorable pasts may find it harder to improve their financial standing due to predictive models that limit access to credit or affordable insurance.

Data Broker Landscape Consolidation via Acquisitions Like Lexisnexis's Seisint Purchase

This section explores the consolidation of the data brokerage landscape, highlighting how aggressive acquisitions, like LexisNexis's acquisition of Seisint, concentrated power in the hands of a few major players. Motivated to secure a dominant position in the burgeoning "risk" market, these companies, traditionally rooted in legal information or credit reporting, began aggressively swallowing up their smaller rivals, acquiring not only their data repositories but also their valuable intellectual property and technical expertise. The author cites LexisNexis's $775 million acquisition of Seisint, propelled by Seisint's sophisticated data fusion platform and its lucrative contracts with government agencies, as a pivotal moment in this consolidation process. Funk cautions that the reduced competition within this sector further erodes privacy, giving these information giants unprecedented dominance over our personal data and pushing them even further out of regulators' grasp.

Practical Tips

• You can monitor industry trends by setting up a personalized news feed with keywords like "data brokerage," "acquisitions," and specific company names of interest. Use a free news aggregator app to receive daily updates, which will help you understand the market dynamics and identify patterns similar to the LexisNexis and Seisint acquisition. This way, you can stay informed about consolidation movements without needing deep industry knowledge.
• Choose to shop at local businesses and use services from independent providers. When you make a conscious decision to purchase from small businesses, you're helping to sustain a diverse marketplace. This can range from buying groceries at a local farmer's market to selecting a local independent coffee shop over a multinational chain. Your spending power can help these smaller entities thrive in the shadow of larger competitors.
• Create a "risk contingency" savings account specifically for unexpected life events. Calculate a percentage of your monthly income to set aside in this account. This fund acts as a financial buffer against unforeseen expenses, such as medical emergencies or sudden job loss, ensuring that you have a financial safety net in place.
• Start a collaborative tech blog or podcast to tap into the collective intellectual property and technical expertise of a community. By inviting guest contributors who are experts in their fields, you create a knowledge-sharing ecosystem. This mirrors the way companies assimilate expertise through acquisitions, and it allows you to learn from the best practices and insights of others, effectively broadening your own skill set and understanding of various technical domains.
• Engage in a role-playing exercise with friends or family where you simulate the negotiation process of a major acquisition. Assign roles such as the CEO of the acquiring company, the company being acquired, and financial advisors. This activity will help you grasp the complexities and strategic considerations involved in such transactions, enhancing your understanding of what makes an acquisition pivotal for the companies involved.
• You can evaluate the potential of your own ideas by researching if there are government grants or contracts that align with them. If you have an innovative concept or a business idea, look into government websites that list available contracts and grants. See if there's a match with your idea's sector, technology, or service. This can be a strong indicator of your idea's potential for success and attractiveness to larger companies for acquisition.
• Start supporting small businesses and startups that prioritize privacy to encourage competition in the sector. When you choose to buy products or services from companies that make privacy a selling point, you're voting with your wallet for a competitive market that values user privacy. Look for new entrants in the market that are transparent about their data practices and support them by becoming a customer or recommending them to your network.
• Advocate for transparency and accountability by supporting organizations that monitor and challenge the power of information giants. Find and donate to non-profits that work towards digital rights and fair data practices. Your contribution helps sustain efforts to hold these companies responsible for their actions.

How Asher's Tools Affected Surveillance, Voter Suppression, Security, Law Enforcement, and Social Control

Here, Funk examines the wide-ranging impacts of Asher's data technologies, highlighting how the tools he created have been used not only for their stated noble purposes but also for voter suppression, racial profiling, and other forms of societal domination. This section delves into how his systems were employed to purge voter rolls of minority voters in the 2000 election, how they were integrated into predictive policing algorithms, and the ethical concerns surrounding the use of data fusion to predict criminal behavior or terrorism.

Asher's Tools in Removing and Suppressing Voters

This section details how Asher's technology for integrating data, originally designed for commercial purposes, was used for political ends, particularly in the development of lists for purging voter registrations that disproportionately targeted minority voters.

DBT "Purge List" Targeting Minorities in the 2000 Election

Funk criticizes Asher’s DBT being used to create a "purge list" of purported felons in Florida for the presidential election in 2000. Although the initiative was promoted as a way to maintain the integrity of voter rolls, the author exposes the inherent flaws in the process of data matching and the deliberate loosening of confidence thresholds that led to thousands of eligible voters—mostly Black and Democratic-leaning—being wrongfully removed from the system. Funk points to this incident as a chilling example of how data-driven tools can be weaponized for political gain, contributing to a climate of losing voting rights and eroding faith in the democratic process.

Practical Tips

• You can enhance your critical thinking skills by creating a personal checklist to evaluate the reliability of databases and lists you encounter in daily life. This might include checking the source of the information, the methodology used to compile it, and cross-referencing with other data sources. For instance, when you come across a list of local service providers, use your checklist to assess its credibility before making decisions based on it.
• Set a "confidence threshold" for everyday tasks and then deliberately lower it. If you usually wait for 90% certainty before sending an email or making a phone call, try acting on 70% confidence. Track the outcomes to see if the lower threshold still leads to successful results, which can recalibrate your need for absolute certainty.
• Volunteer as a poll worker to ensure fair voting practices at your local precinct. By becoming a poll worker, you'll be directly involved in the election process, where you can help safeguard the integrity of voter registration and ensure that all eligible voters can cast their ballots. This hands-on approach allows you to witness and address any discrepancies or wrongful removals firsthand.
• You can educate yourself on voting rights by following non-partisan organizations that focus on voter education and rights. By staying informed through their resources, you'll understand the current challenges and changes in voting laws. For example, subscribe to newsletters from groups like the League of Women Voters or the Brennan Center for Justice, which provide updates and analyses on voting rights issues.

Other Perspectives

• The initiative's promotion as a tool for integrity might not account for the socio-economic and racial disparities that could arise from its implementation.
• Human oversight and manual verification processes can be incorporated to review potential mismatches, thereby reducing the likelihood of errors in data matching initiatives.
• The incident in question may reflect a failure in oversight and governance rather than an inherent issue with data-driven technology itself.
• The event might have led to increased scrutiny and improvements in the voter registration process, thereby strengthening the integrity of future elections.

Incorporating Asher's Data Into Algorithms and Heat Lists for Predicting Crimes

This section examines how the logic underlying Asher's terrorist prediction algorithm was incorporated into modern crime prediction strategies. Drawing on case studies from Chicago and Los Angeles, where "heat lists" and similar risk scores were used to evaluate individuals' propensity for criminal behavior, Funk critiques the inherent biases baked into these systems and their reliance on statistics that can perpetuate cycles of racial stereotyping and overpolicing of minority communities. He argues that these technologies, while often presented as tools for public protection, have the unintended effect of reinforcing existing inequities and criminalizing poverty. Funk calls for increased transparency in how these systems are developed and deployed and for a more critical approach to the ethical implications of utilizing data to predict individual behavior.

Practical Tips

• You can enhance your personal safety by analyzing patterns in local crime reports and adjusting your routines accordingly. By regularly checking police blotters or community crime maps, you can identify trends such as increased burglaries in certain neighborhoods or times of day when certain crimes are more likely to occur. Use this information to make informed decisions, like avoiding high-risk areas during vulnerable hours or reinforcing your home security during a local burglary spike.
• You can develop a personal risk assessment tool to gauge your own habits and tendencies that may lead to negative outcomes. Start by listing behaviors that you believe could be risky or lead to trouble, such as procrastination or spending too much time on social media. Assign a risk score to each behavior based on how likely it is to impact your life negatively. Regularly review and adjust your scores as your habits change, using this as a self-improvement tool to monitor and motivate positive behavioral changes.
• You can enhance your critical thinking by questioning the validity of statistics presented in news reports on crime. When you encounter a statistic, take a moment to consider what data might be missing, how the sample was selected, and whether the statistic is being used to support a particular narrative. For example, if a report claims a dramatic increase in a specific crime, research historical crime rates in the area to see if the change is part of a longer trend or an anomaly.
• You can diversify your media consumption to include more voices from minority communities. By actively seeking out and engaging with content created by and for minorities, such as podcasts, books, movies, and social media accounts, you'll gain a broader perspective that challenges stereotypes. For example, subscribe to a podcast that discusses social justice issues from the viewpoint of minority communities or follow social media influencers who advocate for racial equality.
• Conduct a personal audit of the technologies you use that are intended for safety and protection. Make a list of these technologies, such as home security systems, car alarms, or even apps on your phone that track your location for safety reasons. Reflect on how they might contribute to broader societal inequities and consider making changes to your usage habits or supporting alternative solutions that promote fairness and inclusivity.
• Create a checklist of questions to ask companies when you're signing up for new services or products that use predictive algorithms. These questions could include inquiries about how they collect data, what they use it for, how they ensure its accuracy, and what transparency measures they have in place. By asking these questions, you're not only educating yourself but also signaling to companies that their customers care about these issues.

Asher's Development of Data-Driven Counterterrorism Systems for Government

In this section, Funk delves into Asher's contribution to creating data-driven counterterrorism systems for the U.S. government, highlighting his collaboration with the Central Intelligence Agency and the National Security Agency while examining the ethical implications of using data fusion to predict and preemptively address threats.

Asher's Integration of Technology in Post-9/11 Intelligence Surveillance

The author details Asher's collaboration with the CIA and NSA after 9/11, emphasizing how his expertise in combining data and massively parallel processing helped these agencies develop and enhance their surveillance capabilities. While the specific details of these projects remain shrouded in secrecy, Funk explains that Seisint's technologies and its ECL programming language played a pivotal role in building systems that could process and analyze vast amounts of intelligence collected from numerous sources. He points to job listings requiring knowledge of ECL, years after Seisint was absorbed by LexisNexis, as evidence that Asher's legacy continues within the intelligence community. Funk underscores the ethical concerns of using these powerful tools, highlighting the potential for abuse and the lack of transparency surrounding their deployment.

Practical Tips

• Start a book club or discussion group centered around historical events or fiction that involves intelligence and security themes. This can foster a deeper understanding of the complexities and ethical considerations in intelligence work without needing any background in the field. Through these discussions, you can explore the decision-making processes and moral dilemmas faced by those in the intelligence community, which can be a springboard for broader conversations about security and privacy in your own life.
• You can enhance your home security by setting up a network of smart cameras that use data integration to provide comprehensive surveillance. By connecting different camera feeds to a central system that uses software capable of integrating data, you can monitor multiple angles of your property simultaneously. This approach allows for quicker identification of potential security breaches and more efficient data management.
• Participate in open-source intelligence projects to get hands-on experience. Many projects welcome volunteers to help with data analysis and pattern recognition, which are key components of intelligence systems. By contributing to these projects, you can learn how technologies similar to Seisint's are used in real-world scenarios. Websites like GitHub or SourceForge can be starting points to find projects that are open to public collaboration.
• Create a study group with others interested in intelligence careers to practice ECL. Use social media or community forums to connect with like-minded individuals who also want to learn ECL. Organizing regular virtual or in-person meetups can provide a supportive environment for practicing conversational ECL and sharing resources, such as study materials or insights into the intelligence community's language requirements.
• Start a mentorship chain by teaching someone a unique skill or piece of knowledge you possess, then encourage them to teach another. This mirrors the ripple effect of a legacy within a community. For example, if you're skilled in woodworking, you could teach a friend or a family member how to craft a specific item, and then ask them to pass that knowledge on to someone else, creating a chain of knowledge transmission.
• Engage in conversations with friends and family about the importance of digital privacy. Share tips on how to protect personal information and discuss the implications of data breaches. This could be as simple as starting a chat over dinner about the latest news on data privacy or sharing an article on social media to spark discussion.

Ethical Concerns of Using Data Integration to Foresee Threats

Throughout this section, Funk weaves in a thread of ethical concern about using data synthesis for predicting and preemptively addressing threats, be it terrorism or ordinary crime. Drawing on insights from sociologists and legal scholars, he argues that relying on scores generated by data can lead to a dangerous over-reliance on algorithms, undermining human judgment and potentially exacerbating existing biases within the justice system. Moreover, he points to the questionable efficacy of many predictive policing initiatives, like the Strategic Subjects List in Chicago and Operation LASER in Los Angeles. Funk calls for a more critical approach to these technologies, urging greater transparency in their development and highlighting the necessity of robust oversight to prevent the erosion of civil liberties for the sake of safety.

Context

• Relying heavily on algorithms can undermine human judgment, which is crucial in law enforcement. Human officers can consider context and nuances that algorithms might miss, making a balanced approach important.
• The use of algorithms in threat prediction raises ethical questions about privacy, consent, and the potential for surveillance overreach. Balancing safety with civil liberties is a critical concern.
• The quality and representativeness of the data used in these systems are crucial. Incomplete or skewed data can result in inaccurate predictions, disproportionately affecting marginalized groups.
• There is a call for greater transparency in how predictive policing algorithms are developed and used, including clear explanations of their decision-making processes and regular audits to ensure they do not violate civil rights.
• Funk's call for a critical approach suggests the need for ethical frameworks to guide the development and implementation of these technologies, ensuring they are used responsibly and justly.
• It ensures that the tools comply with legal standards and regulations, which can vary significantly across different jurisdictions.
• Groups such as the American Civil Liberties Union (ACLU) advocate for oversight and transparency in policing technologies, often pushing for reforms and legal challenges to protect individual rights.

The Broader Societal Impacts of Asher's Surveillance Infrastructure

This final section explores the broader societal impacts of Asher's data gathering and fusion technologies, arguing that his work fundamentally contributed to the normalization of mass data collection, the erosion of personal privacy, and the potential for exacerbating systemic biases through predictive analytics.

Normalization of Data Collection and Privacy Erosion

Funk argues that Asher's work at DBT and Seisint played a significant role in normalizing mass data collection and contributing to an erosion of privacy within American society. This section examines how Asher and his contemporaries at companies like Equifax and ChoicePoint systematically dismantled social expectations of privacy by exploiting loopholes in the laws, acquiring vast amounts of sensitive personal information, and demonstrating the commercial and political value of such data. While highlighting the benefits of technology for locating missing children and catching criminals, the author cautions that the normalization of surveillance, particularly when combined with the rapid advancement of facial ID and other monitoring technologies, creates an environment where the potential for misuse is limitless and where the right to be forgotten becomes increasingly unattainable.

Context

• The practices of DBT and Seisint highlighted the need for stronger data protection laws, influencing subsequent debates and legislation around privacy and data security.
• The actions of these companies have contributed to shifting societal norms around privacy. As people become accustomed to widespread data collection, they may become less vigilant about protecting their personal information.
• In the U.S., there is no single, comprehensive federal law governing data privacy. Instead, there are sector-specific laws, such as HIPAA for health information, which leaves many areas unregulated and open to exploitation.
• Nations and companies with access to vast amounts of data can gain a competitive edge in the global market, driving economic growth and technological leadership.
• This concept refers to an individual's ability to have personal information removed from internet searches and databases, particularly when it is no longer relevant or accurate.

Exacerbation of Systemic Biases Through Predictive Analytics

Funk emphasizes how Asher's pioneering work in predictive analytics, while born of good intentions in part, unwittingly paved the way for frameworks that might exacerbate systemic biases and perpetuate existing inequalities. This section explores how biased datasets can cause machine learning models to reproduce and amplify those biases, leading to disproportionate targeting of minority communities by police, immigration agencies, and even healthcare providers. The author cites examples ranging from the use of predictive policing algorithms to find crime-heavy "hotspots" in predominantly Black neighborhoods to the use of social determinants of health to determine which patients are prioritized for critical care during the COVID-19 pandemic. Funk calls for a more ethical approach to data integration and predictive analytics, one that recognizes the technology's limits, prioritizes transparency and accountability, and actively works to correct inherent biases rather than inadvertently amplifying them.

Context

• Systemic biases in predictive analytics often arise from the data used to train models. If the data reflects historical inequalities or prejudices, the models can perpetuate these issues.
• Amplified biases can lead to unfair treatment of individuals or groups, reinforcing stereotypes and systemic inequalities. This can result in discrimination in areas such as law enforcement, healthcare, and employment.
• During the pandemic, algorithms were sometimes used to allocate limited resources like ICU beds or vaccines. If these algorithms relied heavily on social determinants without adjusting for systemic inequities, they could prioritize individuals from more privileged backgrounds, exacerbating health disparities.
• Providing education and training for data scientists and analysts on ethical data practices can help foster a culture of responsibility and awareness regarding bias.
• These are conditions in which people are born, grow, live, work, and age. They can influence health outcomes and should be considered carefully in predictive models to avoid reinforcing health disparities.