152-ethical-moral-and-cultural-issues Flashcards
Ethics and Morals of Digital Technology
Ethics and morals relate to community values and the impact of decisions on different groups in society.
As computers become more integrated into daily life, it’s crucial to consider the ethical, environmental, social, cultural, and moral implications of these changes.
Ethical issues of digital technology on wider society include:
Ill health, distractions, addiction
Digital divide
Social divide
Confidential data
Social pressure to buy and upgrade
High cost
Storing and accessing personal information
Rules and terms for joining
Consequences for misconduct, such as cyberbullying and plagiarism
Communication of inappropriate materials for students, school, and teachers
Backing up data for preservation
Gaining parental consent for online communication
E-safety
Acceptable use policy
Job loss and replacement
Ethics of algorithmic decision-making that could affect people’s lives
Censorship and propaganda
Computers in the workplace
Automation in the form of robotics has almost completely removed the need for human workers
Advantages of computers in the workforce
Machines can work faster, longer, and more accurately and reliably
Computing technology is increasingly allowing more lower skilled jobs to be automated
The skill sets required in a variety of sectors have shifted to hiring fewer, highly skilled technicians to run, support, monitor, and maintain robotic systems
Completing the same job more quickly
Reducing the need for humans to do repetitive tasks
Saving salary costs
Leading to innovative working practices and improved collaboration
Improving efficiency, productivity, and confidence
Lowering labour and wage costs and reducing strain on workers
Having potential to save lives
Providing more knowledge
Creating opportunities for online businesses
Ensuring greater security since fewer people view the data
Creation of new job roles
Computers can complete tasks more quickly than humans, increasing efficiency.
Repetitive and tedious tasks can be automated, allowing humans to focus on more interesting work.
Businesses can save money on salaries by using technology.
Innovative working practices and improved collaboration can be achieved through technology.
Improved productivity and knowledge can be gained from using computers.
Online businesses can offer opportunities for people.
With the Internet becoming accessible to almost everyone, there has also been a rise in the services being offered exclusively online.
reduced costs of renting a physcial place
Disadvantages of computers in the workforce
Automation in the form of robotics has almost completely removed the need for human workers
Costing money to invest in computer systems
Causing people to lose their jobs - unemployment
Inability to improvise or think on their feet
Inability to program years of human experience
Job losses due to branch closures
Changes in work practices
Changes in required skill sets
Computerisation has contributed to high levels of structural unemployment, especially in middle-income manufacturing jobs.
A significant shift towards low-income service jobs has occurred due to computerisation.
A dependence on computers in the workplace can lead to a major loss of output if something goes wrong.
Investing in computer systems can be expensive.
People can lose their jobs due to automation.
Computers cannot improvise or think on their feet, and programming years of human experience is currently not possible.
Computers in the workplace (online banking example)
Impact on customers: 24/7 access to banking services, instant decisions, less personal service, susceptible to hacking
Impact on bank staff: job losses due to branch closures, creation of new job roles, changes in work practices, changes in required skill sets
Impact on banks: fewer overheads, targeted marketing, data protection responsibilities
Impact on local communities: small businesses may see less revenue, elderly and vulnerable customers value local services
Automated decision making
Computers are being used to make decisions that were previously left to humans.
Pros of automated decision making
Decisions can be made instantly.
A computer can be trusted to follow a set of rules to the letter.
Computers are unaffected by emotion.
Computers don’t get tired or have bad days.
Algorithms have improved productivity and made certain processes more convenient.
Automated decision making in driverless cars has the potential to save lives.
Issues with automated decision making
Ethical issues in healthcare where automated systems can recommend treatment, but technology should only augment or support qualified professionals’ decision-making.
Examples of automated decision making
Stock market transactions carried out automatically via automatic trading.
Real-time price comparison sites often entirely automated.
Vineyards using remote sensors to track moisture, rainfall, and soil conditions to decide optimal harvest times.
Artificial intelligence (AI)
Describes any machine programmed to think, behave, work and react like a human.
Two categories: Applied/weak/narrowed AI and Generalised AI.
Designed to manage a specific task.
Most common form of AI.
E.g., image recognition.
Can evolve and improve to handle other tasks.
Emerging and developing area of AI.
Closely linked to machine learning.
Robotics:
A machine that carries out work by itself by following a set of programmed rules.
Dumb robot: Simplify repeats the same task over and over - no AI involved.
Smart robot: Trained to learn, adapt and carry out progressively more complex tasks.
AI uses
recruitment: Put possible candidates through an automated set of tests designed to assess their personality, intelligence and risk tolerance.
Those who pass are then invited to an interview with a human recruiter.
Claim that software fairly and accurately measures cognitive and emotional attributes in a quick time.
Ethical to judge people based on an entirely computer-controlled process?
Self-driving cars - The AI algorithm behind self-driving cars will massively reduce the number of road traffic-related deaths.
The AI algorithms behind self-driving cars will cause unavoidable deaths.
Adapting revision content based on a student’s prior success and failures.
Predicting when to make stock market trades.
Suggesting products a user might want to purchase.
Alerting an operator about fixes and errors in manufacturing.
AI cons
Leading figures believe that the threat of computers “taking over” is very real indeed.
Automated decision making is used to determine what users should be displayed on their social media feeds.
Algorithms can be used to make decisions that have life-changing consequences.
Relying entirely on these algorithms could result in people being treated unfairly.
Algorithmic decision-making in driverless cars raises ethical questions about how to decide who should be harmed if a scenario arises.
Algorithmic Bias:
Designing an algorithm to prioritize certain outputs or favor one group of users over another, which can lead to unfair treatment
Accountability.
Legal liability.
Safety.
Algorithmic bias.
Safety:
How can we ensure safety with the implementation of an algorithm that can choose, learn and adapt?
What rules should be programmed to make sure it does no harm, and what it should do when harm is unavoidable?
Accountability:
The choices the AI makes will have consequences.
Who should be held accountable for the actions carried out by a smart AI algorithm?
Legal liability:
In the case of loss of life/injuries, who should be held legally responsible - the person who purchased the car, the programmer, the manufacturer, or the government?
Machine learning:
The ability to learn without being explicitly programmed.
Subset of AI and one way to achieve artificial intelligence.
Achieved by feeding it data, information and scenarios so it can learn over time.
expert systems (AI)
Expert systems, also known as knowledge-based systems, replicate the knowledge and experience an expert in a particular subject would have. They are made up of a knowledge base which consists of a set of facts and rules. This is interrogated to find diagnoses.
neural networks
One of the most common uses of AI is neural networks which ‘learn’ from a set of data that they are given. This knowledge can be applied to new data sets and is used in pattern detection and picking up on financial fraud
Environmental effects of computing technologies:
Manufacturing computing technologies demand natural finite resources
Disposal of computing technologies exposes poor people to dangerous chemicals
Energy consumption of computing technology contributes to global warming - especially in storing data online in huge cloud data centres
Natural resource management, energy consumption and technology disposal are environmental concerns