Use of Facial Recognition Technology by Law Enforcement
First, identify one piece of big data (or big data use case) of social relevance. Be specific!
Good: a Tim Hortons app as an example of location data collection practices
Good: Facebook’s controversial shutdown of NYU Ad Observatory
Bad: ‘everything Google knows about you’
Second, briefly describe this piece of big data:
Where does the data come from? Who produces it, and how?
Who makes what kinds of claims and decisions on the basis of this data?
What are the major human stakes/implications of how this data is/might be used?
Third, outline three possible ways you could undertake a critical analysis of this data. Think back to our Week 2 readings: critical does not mean negative, but focuses on the social, cultural, moral, and human implications of technologies, rather than just how they ‘work’ at a technical level or their commercial logic.
For each of the three methods, clearly state how practically this might be done, and what you would learn from that research that you would not learn from other methods. The point here isn’t to make sweeping arguments about the nature of the internet, but to report in detail on what you would do if you were to conduct a full scale research paper on this piece of data.
Good example: My chosen data is BC COVID infection rates. One path might involve interviewing local business owners to better understand how they adapt their risk assessments. Another path might scrape social media for a random sample of posts, to spot broader statistical patterns in public opinion. Here is an example of a recent academic journal article that does something similar...
Submission rules and deadlines
Submit a 700 word (minimum 650 words, maximum 750 words)
Your investigation should cite at least 3 pieces of academic research (peer-reviewed), using APA citation style. You may use course materials for this assignment if you wish
The Chosen Data Set Uses Facial Recognition Technology by Law Enforcement Agencies for Surveillance Purposes
Student name
Institution
Professor
Course
Date
The Chosen Data Set Uses Facial Recognition Technology by Law Enforcement Agencies for Surveillance Purposes
Description of the Data Set
Multiple sources provide facial recognition data, including surveillance camera footage, social media, public databases, and other sources- police and security agencies generate this data. Facial recognition systems identify people based on their traits. Law enforcement databases and software systems provide access. According to these statistics, facial recognition technology helps police prevent crime, identify suspects, and protect the public. They select surveillance cameras, facial recognition software, and suspects. Data-driven claims and choices affect privacy, civil liberties, and social fairness. The usage of this data raises questions about privacy, technical biases, and underprivileged communities. Facial recognition poses issues of consent, monitoring, and abuse. It can unfairly target particular groups.
Three Possible Research Methods
Ethical Analysis
This method analyzes police usage of facial recognition technology ethically. Privacy, autonomy, fairness, and social justice will be examined (Raji et al., 2020). The researcher could interview law enforcement officials, citizens, and civil rights organizations about the technology's potential effects, ethical frameworks, and opinions. The researcher must evaluate facial recognition technology-related ethical frameworks and principles to conduct an ethical analysis (Raji et al., 2020). Privacy, consent, non-discrimination, and procedural fairness are examples. The researcher would compare these ideas to law enforcement's facial recognition technology techniques. This method helps the researcher understand facial recognition technology's social, cultural, moral, and human aspects.
Social, Cultural, Moral, and Human Factors
The researcher can examine facial recognition technology's social, cultural, moral, and human impacts using ethical analysis. It illuminates individual rights, societal norms, power relations, and prejudice (Raji et al., 2020). Research can illuminate complicated ethical issues and inform policy by addressing multiple viewpoints and engaging with varied stakeholders. Facial recognition technology affects social relationships, structures, and interactions (Raji et al., 2020). Facial recognition technologies can increase community surveillance, changing trust and privacy dynamics.
Impact Assessment
This method evaluates facial recognition technology's effects on people and communities. It would require collecting empirical data on facial recognition surveillance's effects on underprivileged communities (Penfield et al., 2013). Interviews, surveys, and focus groups could examine the technology's psychological, social, and cultural effects. The researcher would interview facial recognit...