Recent research from Columbia University challenges the belief that each fingerprint is unique. The university’s team trained an AI tool to analyze 60,000 fingerprints and determine whether prints from different fingers belonged to the same person. The technology was able to identify this with 75-90% accuracy, although the researchers are unsure of the exact method used by the AI. The tool seemed to focus on the orientation of the ridges in the center of the finger rather than the minutiae, which are the individual ridges’ endings and forks. This approach differs from traditional forensic methods.

The results of this study could have implications for biometrics and forensic science. Currently, if an unidentified thumbprint is found at one crime scene and an unidentified index finger print at another, they cannot be forensically connected. However, the AI tool could potentially identify the connection. The researchers acknowledge that more research is needed, as the tool is not yet suitable for court cases. It is better suited for generating leads in forensic investigations.

Dr. Sarah Fieldhouse, an associate professor of forensic science, raises questions about the AI tool’s markers and their consistency. She wonders if the markers remain the same when the skin twists upon contact with the print surface and over a person’s lifetime. The researchers themselves are uncertain about how the AI tool works, as is often the case with AI-driven tools.

The Columbia University study has been peer-reviewed and will be published in the journal Science Advances. However, it is important to note that the study’s findings may not have a significant impact on criminal casework at this stage. Despite this research, the belief that fingerprints are unique remains widely accepted.

Original news source: Our fingerprints may not be unique, claims AI (BBC)

🎧 Listen:

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📖 Vocabulary:

Group or Classroom Activities

Warm-up Activities:

– News Summary
Instructions: In pairs, have students read the article and then summarize the main points in their own words. They should focus on the research findings, the implications for biometrics and forensic science, and the uncertainties surrounding the AI tool.

– Opinion Poll
Instructions: Divide the class into small groups. Have each group discuss and debate the following question: “Do you think the research findings challenge the belief that fingerprints are unique?” After the discussion, each group should conduct a poll within their group to determine the majority opinion. Then, have a representative from each group share their group’s opinion and the poll results with the whole class.

– Pros and Cons
Instructions: Divide the class into two groups – one group representing the pros and the other representing the cons. Each group should brainstorm and discuss the advantages and disadvantages of the AI tool’s ability to connect fingerprints from different fingers. After the discussion, have each group present their arguments to the class. Encourage a respectful debate and discussion among the students.

– Keyword Taboo
Instructions: Write down key words from the article on separate cards or pieces of paper. Divide the class into two teams. One student from each team will come to the front of the class. The teacher will show them a card with a keyword on it, and they will have to describe the word to their team without saying the keyword. The team members will try to guess the keyword. The team that guesses correctly in the shortest amount of time gets a point. Continue until all the keywords have been used.

– Future Predictions
Instructions: In pairs or small groups, have students discuss and make predictions about the future of fingerprint analysis and biometrics. They should consider the potential advancements in technology, the impact on forensic investigations, and any ethical concerns that may arise. After the discussion, have each group share their predictions with the class. Encourage students to support their predictions with reasoning and evidence from the article.

🤔 Comprehension Questions:

1. What did the research from Columbia University challenge?
2. How many fingerprints did the AI tool analyze?
3. What was the accuracy rate of the AI tool in determining whether prints belonged to the same person?
4. What aspect of the fingerprints did the AI tool focus on?
5. What could be the potential implications of this research for biometrics and forensic science?
6. Why are the researchers unsure of the exact method used by the AI tool?
7. What is the current limitation in connecting thumbprints and index finger prints forensically?
8. What type of investigations is the AI tool better suited for, according to the researchers?
Go to answers ⇩

🎧✍️ Listen and Fill in the Gaps:

Recent research from Columbia (1)______ challenges the belief that each (2)______ is unique. The university’s team (3)______ an AI tool to analyze 60,000 fingerprints and determine whether prints from different fingers belonged to the same person. The technology was able to identify this with 75-90% accuracy, although the researchers are unsure of the (4)______ method used by the AI. The tool seemed to focus on the orientation of the ridges in the center of the (5)______ rather than the minutiae, which are the individual ridges’ (6)______ and forks. This approach differs from (7)______ forensic (8)______.

The results of this study could have implications for biometrics and forensic science. Currently, if an unidentified thumbprint is found at one crime scene and an unidentified index finger print at another, they cannot be forensically connected. However, the AI tool could potentially identify the connection. The researchers acknowledge that more research is needed, as the tool is not yet suitable for court cases. It is better suited for generating (9)______ in forensic investigations.

Dr. Sarah Fieldhouse, an associate professor of forensic science, raises questions about the AI tool’s markers and their consistency. She wonders if the markers (10)______ the same when the skin twists upon (11)______ with the print (12)______ and over a person’s lifetime. The researchers themselves are uncertain about how the AI tool works, as is often the case with AI-driven (13)______.

The (14)______ University study has been peer-reviewed and will be published in the journal Science Advances. However, it is (15)______ to note that the study’s findings may not have a significant impact on criminal casework at this stage. Despite this research, the belief that (16)______ are unique remains widely accepted.
Go to answers ⇩

💬 Discussion Questions:

Students can ask a partner these questions, or discuss them as a group.

1. What is your opinion on the belief that each fingerprint is unique?
2. How would you feel if you found out that your fingerprint was not as unique as you thought?
3. Do you think it is important for forensic science to accurately connect thumbprints and index finger prints from different crime scenes? Why or why not?
4. Have you ever had your fingerprints taken for any reason? How did you feel about it?
5. Do you think biometrics, such as fingerprints, are a reliable form of identification? Why or why not?
6. How do you think the AI tool analyzes the orientation of the ridges in the center of the finger?
7. Do you trust AI-driven tools in forensic investigations? Why or why not?
8. What other markers do you think could be used to identify fingerprints besides the ridges and minutiae?
9. How do you think the twisting of the skin upon contact with the print surface could affect the markers used to identify fingerprints?
10. Do you think the AI tool’s method of focusing on the orientation of the ridges is more effective than traditional forensic methods? Why or why not?
11. How do you think this research could impact the future of biometrics and forensic science?
12. Do you think this research could potentially lead to wrongful convictions or releases in criminal cases? Why or why not?
13. Have you ever heard of any other scientific beliefs that were widely accepted but later proven to be incorrect?
14. How do you think the belief that fingerprints are unique became so widely accepted?
15. Do you think it is important for the general public to be aware of this research and its findings? Why or why not?

Individual Activities

📖💭 Vocabulary Meanings:

Match each word to its meaning.

Words:
1. challenges
2. unique
3. analyze
4. accuracy
5. orientation
6. minutiae
7. implications
8. biometrics
9. forensic
10. unidentified
11. generating
12. associate
13. consistency
14. peer-reviewed
15. casework

Meanings:
(A) Pertaining to the application of scientific methods and techniques in crime investigations
(B) Consequences or effects that are likely to happen as a result of something
(C) Related to the measurement and analysis of unique physical or behavioral characteristics
(D) The degree to which the result of a measurement or calculation conforms to the correct value or standard
(E) Evaluated by others in the same field to ensure quality and validity
(F) The quality of being consistent, coherent, and uniform
(G) The work or tasks involved in a particular job, often related to legal or social work
(H) Being the only one of its kind; unlike anything else
(I) Someone who is a partner or colleague in a profession or business
(J) Not recognized or identified
(K) Small or trivial details that are usually complex and intricate
(L) The position or alignment of something
(M) Producing or bringing into existence
(N) Examine methodically for purposes of explanation and interpretation
(O) Calls into question or contests
Go to answers ⇩

🔡 Multiple Choice Questions:

1. What did recent research from Columbia University challenge?
(a) The belief that each fingerprint is unique
(b) The effectiveness of AI tools in analyzing fingerprints
(c) The accuracy of traditional forensic methods
(d) The connection between unidentified thumbprints and index finger prints

2. How accurate was the AI tool in determining whether prints from different fingers belonged to the same person?
(a) 50-60%
(b) 75-90%
(c) 30-40%
(d) 10-20%

3. What aspect of the fingerprints did the AI tool focus on?
(a) The minutiae, which are the individual ridges’ endings and forks
(b) The overall pattern of the fingerprints
(c) The orientation of the ridges in the center of the finger
(d) The size and shape of the fingerprints

4. What could be a potential implication of the AI tool in biometrics and forensic science?
(a) Replacing traditional forensic methods entirely
(b) Increasing the accuracy of fingerprint analysis to 100%
(c) Identifying connections between unidentified thumbprints and index finger prints
(d) Eliminating the need for fingerprint analysis in criminal investigations

5. What is the AI tool currently better suited for?
(a) Identifying connections between unidentified thumbprints and index finger prints
(b) Generating leads in forensic investigations
(c) Analyzing fingerprints in court cases
(d) Replacing traditional forensic methods entirely

6. What questions does Dr. Sarah Fieldhouse raise about the AI tool’s markers?
(a) Whether they remain the same when the skin twists upon contact with the print surface and over a person’s lifetime
(b) Whether they are accurate in identifying connections between fingerprints
(c) Whether they can be used as evidence in court cases
(d) Whether they are consistent across different individuals

7. What is the researchers’ level of certainty about how the AI tool works?
(a) Certain
(b) Confident
(c) Indifferent
(d) Uncertain

8. What is the current impact of the Columbia University study on criminal casework?
(a) Significant
(b) Unknown
(c) Inconclusive
(d) Not significant

Go to answers ⇩

🕵️ True or False Questions:

1. The university’s team trained an AI tool to analyze 60,000 fingerprints and determine whether prints from different fingers belonged to the same person.
2. The AI tool could potentially identify connections between unidentified thumbprints and index finger prints found at different crime scenes.
3. The AI tool is already suitable for court cases, and is best suited for generating leads in forensic investigations.
4. Recent research from Columbia University supports the belief that each fingerprint is unique.
5. The researchers themselves are uncertain about how the AI tool works, as is often the case with AI-driven tools.
6. The tool seemed to focus on the minutiae rather than the orientation of the ridges in the center of the finger.
7. The technology was unable to identify this with 75-90% accuracy, and the researchers are unsure of the exact method used by the AI.
8. The results of this study could have implications for biometrics and forensic science.
Go to answers ⇩

📝 Write a Summary:

Write a summary of this news article in two sentences.

Writing Questions:

Answer the following questions. Write as much as you can for each answer.

1. What did the recent research from Columbia University challenge?
2. How accurate was the AI tool in determining whether fingerprints from different fingers belonged to the same person?
3. What aspect of the fingerprints did the AI tool focus on?
4. What potential implications could the results of this study have for biometrics and forensic science?
5. What questions does Dr. Sarah Fieldhouse raise about the AI tool’s markers and their consistency?

✅ Answers

🤔✅ Comprehension Question Answers:

1. The research from Columbia University challenged the belief that each fingerprint is unique.
2. The AI tool analyzed 60,000 fingerprints.
3. The accuracy rate of the AI tool in determining whether prints belonged to the same person was 75-90%.
4. The AI tool focused on the orientation of the ridges in the center of the finger rather than the minutiae.
5. The potential implications of this research for biometrics and forensic science could be the ability to connect fingerprints from different crime scenes to the same person.
6. The researchers are unsure of the exact method used by the AI tool because it is often the case with AI-driven tools.
7. The current limitation in connecting thumbprints and index finger prints forensically is that they cannot be forensically connected if found at different crime scenes.
8. The AI tool is better suited for generating leads in forensic investigations, according to the researchers.
Go back to questions ⇧

🎧✍️✅ Listen and Fill in the Gaps Answers:

(1) University
(2) fingerprint
(3) trained
(4) exact
(5) finger
(6) endings
(7) traditional
(8) methods
(9) leads
(10) remain
(11) contact
(12) surface
(13) tools
(14) Columbia
(15) important
(16) fingerprints
Go back to questions ⇧

📖💭✅ Vocabulary Meanings Answers:

1. challenges
Answer: (O) Calls into question or contests

2. unique
Answer: (H) Being the only one of its kind; unlike anything else

3. analyze
Answer: (N) Examine methodically for purposes of explanation and interpretation

4. accuracy
Answer: (D) The degree to which the result of a measurement or calculation conforms to the correct value or standard

5. orientation
Answer: (L) The position or alignment of something

6. minutiae
Answer: (K) Small or trivial details that are usually complex and intricate

7. implications
Answer: (B) Consequences or effects that are likely to happen as a result of something

8. biometrics
Answer: (C) Related to the measurement and analysis of unique physical or behavioral characteristics

9. forensic
Answer: (A) Pertaining to the application of scientific methods and techniques in crime investigations

10. unidentified
Answer: (J) Not recognized or identified

11. generating
Answer: (M) Producing or bringing into existence

12. associate
Answer: (I) Someone who is a partner or colleague in a profession or business

13. consistency
Answer: (F) The quality of being consistent, coherent, and uniform

14. peer-reviewed
Answer: (E) Evaluated by others in the same field to ensure quality and validity

15. casework
Answer: (G) The work or tasks involved in a particular job, often related to legal or social work
Go back to questions ⇧

🔡✅ Multiple Choice Answers:

1. What did recent research from Columbia University challenge?
Answer: (a) The belief that each fingerprint is unique

2. How accurate was the AI tool in determining whether prints from different fingers belonged to the same person?
Answer: (b) 75-90%

3. What aspect of the fingerprints did the AI tool focus on?
Answer: (c) The orientation of the ridges in the center of the finger

4. What could be a potential implication of the AI tool in biometrics and forensic science?
Answer: (c) Identifying connections between unidentified thumbprints and index finger prints

5. What is the AI tool currently better suited for?
Answer: (b) Generating leads in forensic investigations

6. What questions does Dr. Sarah Fieldhouse raise about the AI tool’s markers?
Answer: (a) Whether they remain the same when the skin twists upon contact with the print surface and over a person’s lifetime

7. What is the researchers’ level of certainty about how the AI tool works?
Answer: (d) Uncertain

8. What is the current impact of the Columbia University study on criminal casework?
Answer: (d) Not significant
Go back to questions ⇧

🕵️✅ True or False Answers:

1. The university’s team trained an AI tool to analyze 60,000 fingerprints and determine whether prints from different fingers belonged to the same person. (Answer: True)
2. The AI tool could potentially identify connections between unidentified thumbprints and index finger prints found at different crime scenes. (Answer: True)
3. The AI tool is already suitable for court cases, and is best suited for generating leads in forensic investigations. (Answer: False)
4. Recent research from Columbia University supports the belief that each fingerprint is unique. (Answer: False)
5. The researchers themselves are uncertain about how the AI tool works, as is often the case with AI-driven tools. (Answer: True)
6. The tool seemed to focus on the minutiae rather than the orientation of the ridges in the center of the finger. (Answer: False)
7. The technology was unable to identify this with 75-90% accuracy, and the researchers are unsure of the exact method used by the AI. (Answer: False)
8. The results of this study could have implications for biometrics and forensic science. (Answer: True)
Go back to questions ⇧