Basic HTML Version

|  | Table of Contents |  | View Full Version

Page 64 - CT_AI_Class-8
P. 64

    UNDERSTANDING BIAS                                                                                                        Let us consider a scenario.

                      Computer  scientist  Joy  Buolamwini  tested  AI technology  used  by  major  tech  companies  to                         A school is developing an AI system to recommend after-school clubs to students. The system uses
                      recognise faces. She found that while the systems worked well for light-skinned men, they made                            past data about which students have enjoyed various clubs. The following training data is used:
                      more errors when identifying women and people with darker skin tones. The reason was clear: the                                                         Club              Boys              Girls
                      systems were trained on datasets that contained far more images of certain groups than others.
                      In one demonstration, the computer failed to detect her face until she wore a white mask. This                                                        Football             60                20
                      highlighted how AI systems can be biased and unfair if the training data does not include a wide                                                         Art                15               55
                      representation of all groups.
                                                                                                                                                The AI program studies the data and learns that most boys enjoy playing football and most girls
                      Bias in Datasets                                                                                                          enjoy art. Now, imagine a new girl student joins the school.

                      Bias in datasets occurs when certain groups are overrepresented or underrepresented, leading                              Think:                                                                          fact bits
                      to  unfair  outcomes.  Bias in  datasets  occurs  when certain  groups  are  overrepresented  or                            Which club will the AI probably recommend to new students             A study by the MIT Media
                      underrepresented, leading to unfair outcomes. The consequences of bias include unequal model                                  after studying the given data?                                         Lab found that facial
                      performance, discrimination and loss of trust.                                                                                                                                                     recognition systems had a
                                                                                                                                                  Is the AI making the recommendation based on ability or               34.7% error rate for dark-
                                                                                                                                                    data pattern?                                                       skinned women, highlighting

                                                                                                                                                  Do you think every student gets an equal chance through                 the need for balanced
                                                                                                                                                    this AI recommendation system?                                             training data.

                                                                                                                                                  How can the data be improved to help the AI make better
                                                                                                                                                    and fairer suggestions?

                                                                                                                                                                                            Ask
                                                                                                                                                                                                      AGENT
                                                                                                                                                                                       OrangeAI
                                                                                                                                                  If an AI makes a biased decision, who should be held responsible—the AI or the humans who trained it?

                                                                                                                                                          Study
                                Bee is a wrist-worn AI device that records conversations and creates
                                summaries,  reminders  and  personal  insights.  It  automatically
                               transcribes tasks and sends the information to a companion app for                                               ENSURING FAIRNESS
                        ai     easy review.                                                                                                     A company once created an AI system to help recruit new employees. The system learned from

                                                                                                                                                past hiring data, which showed that most of the engineers hired in the past were men. Because of
                           lens
                                                                                                                                                this pattern, the AI started to prefer resumes that looked similar to those of male applicants. This
                      AI Systems and Their Use                                                                                                  is a good example of how AI can pick up human biases from historical data if the developers are

                      AI systems are already being used in many areas of life, such as recommending videos, approving                           not careful.
                      loans, assisting doctors and selecting job applicants. However, if the data used to train these AI                        This situation shows that AI can unintentionally favour certain groups, even though it is supposed
                      systems is biased, the AI could make unfair or incorrect decisions.
                                                                                                                                                to treat everyone fairly. To prevent this, developers need to ensure that AI systems are trained
                      For example, imagine an AI system that helps companies choose job candidates. If the past hiring                          with balanced and diverse data.
                      data mostly includes men, the AI might learn to associate men with being better candidates, even                          AI should be designed to benefit everyone, not just a particular group, so that all applicants have
                      though this is not true. This can result in the AI unfairly favouring men over women, even if both                        an equal chance, regardless of their gender, race or background. This helps create a fairer and
                      are equally qualified for the job. This shows how important it is to ensure that the data used to                         more inclusive system.
                      train AI is balanced and fair, so that the system makes decisions based on facts, not biases.

                      62         Artificial Intelligence (CT & AI)-VIII

| 59 60 61 62 63 64 65 66 67 68 69 |