✍️ Coursework (Formative)
2024/25 Autumn Term
🎯 OBJECTIVE: - Work through a real-life case study centered around an AI/data science-related theme
- Format your answers to the questions relating to the case as an HTML document using Quarto Markdown and Zotero. (See 💻 Quarto/Zotero tutorial). Try using a custom theme (see this Quarto documentation page for information on Quarto HTML themes) to make your case study document look more professional.
⌛ DUE DATE: 5 December 2024 5pm.
🗺️ Context
In recent years, just like many organizations, police departements have turned to AI/machine-based solutions to inform their decision-making and make it more objective: this is predictive policing. The following case study will focus on predictive policing.
📚 Case study materials
For this case study, you will rely on the following materials:
🗞️ News articles
- Gavin Stephens (2024) “In England and Wales, where you live determines the kind of policing you get. That isn’t right”. The Guardian – (Stephens 2024)
- Harriet Barber (2024) “Argentina will use AI to ‘predict future crimes’ but experts worry for citizens’ rights”. The Guardian – (Barber 2024)
- Ian Sample (2023)“Met expands use of crime data to focus on most serious criminals”. The Guardian – (Sample 2023)
🎙️ Podcast
- Michael, Safi et al. (2022) “Rapper Nipsey Hussle and the problem of predictive policing”. The Guardian – (Safi et al. 2022)
🎓 Academic articles
- Lyria, Bennett Moses, Janet, Chan (2016) “Algorithmic prediction in policing: assumptions, evaluation, and accountability”. Policing and Society, 28(7), 806–822. –(Moses and Chan 2018)
- Ajay, Sandhu, Pete, Fussey. (2021). “The ‘uberization of policing’? How police negotiate and operationalise predictive policing technology”. Policing & Society, 31(1):66-81. –(Sandhu and Fussey 2021)
- Kristian, Lum, William, S., Isaac (2016) “To predict and serve”. Significance, 13(5):14-19. –(Lum and Isaac 2016)
- Fatima Dakalbab et al (2022) “Artificial intelligence & crime prediction: A systematic literature review”.Social Sciences & Humanities Open, Volume 6, Issue 1, 2022, 100342, ISSN 2590-2911. –(Dakalbab et al. 2022)
❓ Questions
Relying on the materials you have been given, your own knowledge and readings (citing other sources than the ones given is a must: you need to substantiate your answers with at least 3 other references than the ones given), answer the following questions:
- What is predictive policing?
- What does it aim to achieve? And what are its potential benefits, if any?
- What are common algorithms used in predictive policing (provide technical details)? Can you name a example of such an algorithm used in practice? How does it work?
- What are the assumptions that underlie predictive policing algorithms?
- Are those assumptions valid? Why or why not?
- How do people in police departments perceive predictive policing?
- What issues does predictive policing have? What risks does it present?
- How do you think those issues and risks should be mitigated?
- And, finally, what is your take on predictive policing? Is it worth the issues and risks?
When responding to the questions, assume you are writing for a general audience who may not have any background in data science and does not know what DS101A is about.
📝 Instructions
Your answers to the questions must be written in Quarto Markdown. You are to submit an HTML file generated with Quarto Markdown.
Feel free to modify the layout and aesthetics of the Quarto Markdown template. You can also add images, tables, bullet points, etc. to your answers.
Each of your answers need to be properly substantiated with evidence.
You can use part or all of the materials provided but you need to include at least 3 more references than the ones provided in your answers. You must cite these references in your markdown using Zotero (revisit 💻 Quarto/Zotero tutorial).
- Any ideas, arguments or results that were not produced by your mind must be cited in the references.
- 👉 Avoid making explicit references to the course (e.g., writing things like “As we saw in Week 05…”), as this would go against the spirit of the exercise, which is to write to a general audience. Instead, refer to the bibliography we have provided and try to make connections between the ideas we have discussed and the case study. The same goes for AI-generated text.
Make your writing clear, do not hide your thoughts behind jargon. You are not writing an academic article. Your case study is emulating a communication you would send to work colleagues who have very different educational backgrounds. You can find tips on how to write clearly and make your argumentation coherent in the Resources on clean and logical writing section of the 📄 Resources on argumentation and logical fallacies page on the course website.
Do not plagiarise. It is not that difficult to spot that someone copied content from other sources and, frankly, it is very embarrassing if you get caught. Here is the link to the LSE regulation on plagiarism.
- You are allowed to use Generative AI to help you write your essay. But you are asked to report the AI tool you used and the extent to which you used it. Read more about Generative AI in the section below. Check the Generative AI Policy for how reference use of Generative AI in your essay.
Make sure you address all the questions.
🤖 Using AI help?
You are allowed to use Generative AI tools such as ChatGPT to help you write your essay. If you do use it, however minimal use you made, you are asked to report the AI tool you used and add an extra section to your essay to explain the extent to which you used it (this won’t count towards the word limit).
Note that, while these tools can be helpful, they tend to generate responses that sound convincing but are not necessarily correct. Another problem is that they tend to generate responses that are formulaic and repetitive; thus, limiting your chances of getting a high mark.
In effect, you are asked to explain the following:
- What AI tool did you use?
- How did you use it? For example, did you use it to generate ideas, write a draft, proofread your essay, etc.?
- How much of your essay was written by the AI tool? For example, did you feed it the entire prompt and it wrote the entire essay? Or did you feed it guided questions?
- If you didn’t edit the AI tool’s output, what was the output like? For example, did it produce a coherent essay?
- What did you do to make sure that the AI tool did not produce gibberish? and that the essay was not formulaic.
- Importantly, how did you ensure that the essay did not contain any plagiarism?
✅ Submission
- Render your Quarto Markdown file to HTML
- ⚠️ IMPORTANT ⚠️: Rename your HTML to
DS101A-2024-formative-case-study-<CANDIDATE_NUMBER>.html, replacing<CANDIDATE_NUMBER>with your candidate number. For example:DS101A-2024-formative-case-study-123456.html - Upload this file to Moodle under the appropriate assignment.
✋ Getting Help
- If you have any questions about the assignment, please post them on
#helpchannel on Slack. - Book office hours.
- Attend the drop-in session on Wednesday 27th November between 10.30-12 (COL 1.06).
- Organise a study group with your classmates.
📑 Marking Scheme
(You will be graded as if this was a summative assessment.)
The following is the marking scheme we will use to mark your case study. Note that full marks mean that you have met a particular criterion to an extremely high standard, beyond our expectations. If you did “everything right”, you should expect about 70% of the marks on each criterion.
🧮 Understanding of technical theories and algorithms (0-20 marks)
| Marks awarded | Level | Description |
|---|---|---|
| <5 marks |
Fail to demonstrate the technical concepts and algorithms related to the case study |
You barely described any technical concept and/or algorithm related to the case study and if you did, you did so in a very superficial way that showed lack of understanding of the concepts described. |
| 5-10 marks |
Adequate knowledge of the technical concepts and algorithms related to the case study |
You show some high level understanding of the technical concepts and algorithms associated with the predictive policing case study and you are able to convey that understanding to a large extent. However, there are rather major gaps in your technical concepts and algorithms associated with the predictive policing case study. |
| 11-15 marks |
Very good knowledge of technical concepts and algorithms related to the case study |
You clearly understand most, if not all, of the technical concepts and algorithms that underpin the predictive policing case study. However, you might either: - still have minor misconconceptions about some concepts AND/OR - convey the concepts in a way that would be slightly confusing to a general lay audience |
| >15 marks |
🏆 Excellent knowledge of the technical concepts and algorithms related to the case study |
Each of your answers demonstrates a very thorough and detailed understanding of the technical concepts and algorithms that relate to this case study i.e predictive policing. At the same time, you understand them enough to be able to express them in layman’s language. |
🤔 Critical thinking (0-30 marks)
| Marks awarded | Level | Description |
|---|---|---|
| <7 marks |
Complete lack of critical thinking about sources and algorithm used | You don’t show no critical reflection at all on the topics linked to the case study. |
| 7-15 marks |
Limited degree of critical thinking about sources and algorithm used | You present ideas mostly at face value and your reflection on them and critical examination of them remains skin deep and shallow. |
| 16-22 marks |
Some degree of critical thinking about sources and algorithms used | You show some degree of critical thinking relating to the topic of the case study but your critical thinking skills lack some nuance |
| >22 marks |
🏆 Engage critically with sources and algorithms used | You are able to reflect critically on the topic of predictive policing but also question the quality of the materials you are engaging with to build your answers to the case study |
🧬 Organisation and structure (0-15 marks)
| Marks awarded | Level | Description |
|---|---|---|
| <3 marks |
Poor | Information and ideas are poorly sequenced. The audience has difficulty following the thread of thought. |
| 4-7 marks |
Fair | Information and ideas are presented in an order that the audience can mostly follow. |
| 8-11 marks |
Good | Information and ideas are presented in a logical sequence which is followed by the reader with little or no difficulty |
| >11 marks |
🏆 Excellent | Information and ideas are presented in a logical sequence which flows naturally and is engaging to the audience |
🕵️ Use of literature and evidence (0-12 marks)
| Marks awarded | Level | Description |
|---|---|---|
| <3 marks |
Poor | You fail to provide any, or accurate empirical information; you make empirical claims with no evidence to back them up; you use no or inappropriate sources. |
| 4-6 marks |
Fair | You have some difficulties in identifying sufficient or relevant information; insufficient support for empirical claims from reliable sources; use of few or somewhat inappropriate sources. |
| 7-9 marks |
Good | You have some success in making sufficient and relevant empirical claims and in providing sufficient support for them from a reasonable number of reliable sources |
| >9 marks |
🏆 Excellent | You accurately identified sufficient and relevant empirical information, and draw on support from sufficient and reliable sources |
📝 Communication and formatting (0-8 marks)
| Marks awarded | Level | Description |
|---|---|---|
| <3 marks |
Poor | Your Quarto formatting makes it difficult, if not impossible to read your document: major elements of the HTML document are missing. Your writing style is not fit for a general audience. |
| 4-7 marks |
Fair | Your Quarto formatting is very basic (though the document is readable). Your writing is generally too complex for a general audience. |
| 8-11 marks |
Good | You occasionally forget to explain the odd technical concept or abbreviation that a general audience might not be familiar with but your writing style is generally highly legible. Your Quarto formatting is neat, though a few minor elements here and there could be improved. |
| >11 marks |
🏆 Excellent | You customized your Quarto Markdown formatting, including correctly formatted and referenced tables and figured as needed. Your citations are perfectly formatted too. Your writing style is fit for a general audience, free of jargon and excessive abbreviations. |
🎨 Originality in problem solving as a data scientist (0-15 marks)
| Marks awarded | Level | Description |
|---|---|---|
| <3 marks |
Poor | Your answers lack originality: there are no new ideas, insights, or creative synthesis. You primarily reuses others’ work or perspectives without contribution or unique perspective. |
| 4-7 marks |
Fair | Some originality is evident in your answers; you present original thoughts, analyses or perspectives, though they may be less fully developed or occasionally rely on conventional perspectives. |
| 8-11 marks |
Good | You show considerable originality; you present well-developed, unique insights or approaches that enhance understanding of the peculiarities of the case study (i.e its technical aspects and/or its ethical aspects). |
| >11 marks |
🏆 Excellent | You demonstrates a high level of originality with innovative ideas or approaches. Your work is unique, showing creative synthesis or novel application of theories, concepts, or data. |