Cross Cutting Themes Term 2

Importance of minimizing defects and vulnerabilities in software development practices

• Almost every aspect of our modern lives depends on trustworthy software

• Security threats and attack occur almost daily and cost organisations time and money

• Software defects are present in complex-systems:

  • Manifested by design flaws or implementation bugs

  • Exposed under natural-accidental or deliberate conditions

• Software development practices

  • Lack the rigorous controls required to minimize defects into software

  • It is very difficult to produce a bug-free software especially when the software is non-trivial

• Because security is often:

  • Not a priority (Time to market pressure)

  • A financial burden – An afterthought

• The goal is to make a hacker’s job as tough as possible to avoid becoming a victim

• Identify malware threats when designing software and information systems

• Malevolent online practices such as social engineering target vulnerable groups

• Evolving risks to the formerly excluded (older adults) through phishing attacks (2021 the year of breach)

• The silver digital divide. What is it about being ‘old age’ that contributes to reduced technology use?

• Digital divide and the pandemic:

  • What impact did this have on users who were not familiar with these technologies?

Importance of designing systems that mitigate existing digital inequalities

• Inequities between different groups of people may result from the use or misuse of information and technology

• Exclusion: Being locked out of technologies needed to fully participate in society (not reaping economic and social rewards of technology)

• Factors in digital exclusion: ability, affordability and accessibility

• Digital exclusion: recognises differences in technology use as inequity (injustice)

• Digital divide: recognises differences in technology use as inequality only

• Gap between young and old

• Older adult digital exclusion factors

• Inequality vs. Inequity

• Questioning stereotypical views of older adults (aging is not a disease; older adults are not necessarily disabled)

• How digital technologies may reinforce existing inequalities (17)

  • Unequal access (digital exclusion)

  • Unequal outcomes (algorithmic decision-making)

• Algorithmic bias examples (case studies)

  • Medicine and health & criminal justice

• AI and ML increasingly used in medicine and healthcare, often for diagnostic purposes. Often accurate but not for everyone

  • Racial and gender bias examples

  • Why does the gender gap in accuracy exist?

• Predictive policing uses historic crime data to allocate resources geographically

  • Bias in what data is recorded, records are not exact measure of true crime rates, predicts patterns more than it does crime

  • Statistical flaws and social consequences

• Predictive recidivism (criminal reoffending)

  • COMPAS tool provides a risk score to predict likelihood of reoffending

  • Risk score helps determine who is incarcerated and for how long

• Why are these algorithms used? And why are systems designed that way?

  • Accurate and less biased than human decision-making, boost efficiency, aid resource allocation

• What’s at stake here?

  • Different understandings of fairness

  • Can’t satisfy all definitions of fairness simultaneously

  • Fairness vs. predictive accuracy

Valuing privacy; critically reflecting on surveillance and censorship practices with respect to privacy

• Privacy paradox: when people disclose personal information in ways that are inconsistent with the high value they claim to have in privacy

• Stated aim of GDPR: to empower people to more easily and effectively manage their personal data

• Privacy harms: Exposure, Aggregation, Distortion, Exploitation, Exclusion

• Threats to privacy:

  • Surveillance capitalism

  • Data colonialism

  • Behaviour modification

• The slow erosion of privacy

  • “Privacy is rarely lost in one fell swoop. It is usually eroded over time, little bits dissolving almost imperceptibly until we finally begin to notice how much is gone.”

• Do you agree with the definitions given in the lecture on privacy? (How would you define it?)

• Is privacy dead?

  • Yes/No?

  • What would computing systems looks like if we valued privacy?

  • What would software developers and computing professionals (you) need to do to ensure users are protected?

• Is GDPR successful?

• Can you explain how the metaphor of the "panopticon" applies to contemporary digital society?

• The tension between positive freedom (freedom to) and negative freedom (freedom from):

  • How would you balance these?

• How does the concept of the Shock Doctrine apply to surveillance (e.g. policies such as USA Patriot Act)?

• What are some reasonable limits to free speech?

• Can you explain the reason Section 230 exists?

  • What are the criticisms?

  • How are platforms different now to Web 2.0 platforms the law was designed to protect

Emergent key social, ethical and legal challenges and future practices for computing professionals

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