The “dole-bludger” myth refers to a stereotype often used to describe individuals who receive welfare payments, suggesting that they are lazy or fraudulent. However, the article The ‘dole-bludger’ myth can die now — the real cheats were highly paid public servants posits that the real issue lies not with these individuals, but with public servants and policymakers who design and implement flawed welfare systems.
The author invokes the work of French philosopher Michel Foucault, who argued that state power, once overt and physical, has become subtle and bureaucratic, but no less damaging. This reference is used to draw parallels with the robodebt issue, where the harm done to vulnerable individuals wasn’t physically apparent but nonetheless significant.
The term ‘robodebt’ refers to a controversy in Australia where an automated debt recovery system erroneously accused many welfare recipients of owing money to the government. This caused great distress among affected individuals, and the government was widely criticized for its handling of the issue.
The “highly paid public servants” presumably refers to individuals who designed, implemented, or defended the flawed robodebt system. By suggesting these are the “real cheats”, the author appears to be calling for accountability at higher levels of government, arguing that these individuals caused more harm to the welfare system than those they wrongfully accused of defrauding it.
Discipline and Punish
“Discipline and Punish: The Birth of the Prison” is a book by the French philosopher and social theorist Michel Foucault, published in 1975. It’s a deeply influential work in the fields of sociology, criminology, and the philosophy of law.
The central argument of “Discipline and Punish” is that the development of the modern prison is part of a larger shift in how societies exercise power and control. Foucault argues that in the past, power was exercised mainly through spectacular displays of violence, such as public executions, which were meant to deter people from breaking the law.
However, Foucault contends that during the Enlightenment, a new, more subtle form of power emerged. This form of power, which Foucault calls “disciplinary power,” is less about punishing the individual, and more about controlling and regulating populations. Instead of focusing on the body and physical punishment, disciplinary power focuses on the mind and soul through surveillance, normalization, and examination.
In the modern age, according to Foucault, power is largely exercised not through direct physical coercion, but through subtle psychological manipulation and control. This power is exercised in many areas of society, not just in prisons but also in schools, hospitals, and workplaces.
The prison is emblematic of this shift, argues Foucault. Prisons don’t just punish criminals; they aim to transform the criminal’s behavior, to make them ‘normal’. Foucault sees this as a kind of ‘soul training’, using techniques like constant surveillance (the “Panopticon” principle) to instill a sense of self-discipline and self-regulation in the individual.
“Discipline and Punish” is a complex work that covers a lot of ground, but in essence, it is a critique of modern systems of power and control, suggesting that while these systems may appear more humane on the surface, they may in fact be more insidious and controlling than their more overtly violent predecessors.
Discipline and Surveillance in the Digital Age
AI has the potential to both extend and intensify forms of discipline and control, similar to what Michel Foucault described in his book “Discipline and Punish”. Here are a few ways this could happen:
- Surveillance: AI systems can be used for mass surveillance, collecting and analyzing huge amounts of data about individuals’ behaviors and activities. For example, facial recognition technology can identify individuals in public spaces, and social media algorithms can analyze posts and interactions to build comprehensive profiles of individuals. This can create a sort of digital “Panopticon”, a concept from Foucault’s work where the feeling of constant observation instills self-discipline.
- Normalization: AI can be used to enforce social norms and standards. By analyzing and categorizing behavior, AI systems can define what is ‘normal’ and flag deviations. For example, AI could be used to monitor employees’ productivity and flag those who don’t meet certain benchmarks. This could pressure individuals to conform to these standards.
- Predictive Policing: AI algorithms can analyze data to predict where crimes are likely to occur or who is likely to commit a crime. While this could potentially prevent crime, it could also lead to over-policing of certain communities and the unjust targeting of individuals based on flawed or biased data.
- Automated Decision-Making: AI systems are being used to make decisions in areas like hiring, lending, and criminal sentencing. These systems can perpetuate existing biases in the data they’re trained on, leading to unfair outcomes. For example, an AI system used to assess risk in criminal sentencing has been found to be biased against Black defendants.
As AI continues to develop, it’s crucial that we remain vigilant about these potential issues. This includes ensuring transparency in AI systems, combating bias in AI, implementing robust privacy protections, and involving diverse perspectives in AI development and oversight. Moreover, laws and regulations must adapt to these changes, ensuring that AI is used ethically and in a manner that respects human rights.