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Introduction to Artificial Intelligence (AI) complete course is currently being offered by IBM through Coursera platform.
About this Course
In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project.
Also Check: How to Apply for Coursera Financial Aid
AI Issues, Ethics and Bias
Q1) Ethics in artificial intelligence is:
- Something that is not an issue.
- Something that we need to apply today.
- Something that is entirely solved in current AI systems.
- Something that somebody else is going to do in the future.
Q2) One approach that helps developers avoid unintentionally creating bias in AI systems is:
- Using highly specific training data from a narrow range.
- Using a wide variety of appropriately diverse data for training.
- Not using any training data.
Q3) Which of the following statements about IBM’s views on AI are correct?
- Unbiased models and a spirit of diversity and inclusion are necessary to create fair AI systems, which can mitigate, rather than magnify, our existing prejudices.
- AI can be applied to solve some of humanity’s most pervasive problems and create opportunity for all.
- Data and insights belong to the people and businesses who created them. Organizations that collect, store, manage, or process data have an obligation to handle it responsibly.
- Knowing how an AI system arrives at an outcome is key to trust. To improve transparency, we should define how we build, deploy, and manage AI systems through scientific research.
Q4) Which of the following are examples of bias in an AI system?
- AI systems in call centers providing context sensitive assistance to staff.
- Facial recognition systems performing well for individuals of all skin tones.
- Customers not being aware that they are interacting with a chatbot on a company website.
- Image recognition systems associating images of kitchens, shops, and laundry with women rather than men.
Q5) There is concern that some jobs will be replaced by AI systems. Which of the following characteristics make a job a good candidate for replacement?
- Features highly creative tasks.
- Rules-based decision-making.
- Requires innovative problem solving.
- Has very varied, unpredictable tasks.
Q6) Ethical concerns with AI systems are:
- Not genuinely troubling, and the concern of very few AI experts.
- Short term and easily addressed when developing new AI systems.
- Something that can’t be mitigated for.
- Something that should be the concern of every AI developer, so they can be mitigated for as AI systems are developed.
Q7) What are some of the ethical concerns around artificial intelligence?
A. Racial, gender or other types of bias.
B. Loss of jobs due to AI replacing workers performing repetitive tasks.
C. Concern about the trustworthiness of decision-making supported by AI systems.
D. Privacy, for example, as human faces are photographed and recognized in public spaces.
- All of the options are correct
- Only options A, B, and D are correct
- None of the options are correct
- Only options A and B are correct
Q8) Which of the following NOT a way AI is being used to benefit humanity?
- Crime: to identify criminals before they commit a crime.
- In healthcare, AI is being used to predict where the next outbreak of a disease will occur.
- In agriculture, AI is being used to identify and recommend treatment for plant diseases.
- In healthcare, AI is being used to interpret scans for early detection of cancer, eye disease, and other problems.
Q9) How many new opportunities and job roles does the World Economic Forum expect that AI will create in the next few years?
Q10) What is a significant way in which developers of AI systems can guard against introducing bias?
- Using government approved algorithms.
- Using less varied AI systems and datasets.
- Using only examples from their own environment as training data.
- Providing effective training data and performing regular tests and audits.
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