65009 Forensic Inference and Interpretation
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Credit points: 8 cp
Result type: Grade and marks
There are course requisites for this subject. See access conditions.
Description
This subject provides students with an understanding of the evaluation and interpretation of the fundamental unit of forensic science, 'the trace', through probabilistic inferences. Students gain skills in using the logical approach to interpret observations and results in the context of practical forensic examples, case studies, and forensic data. This subject aims to provide students with extensive knowledge in the application of probability theory, principles of evidence evaluation, Bayesian networks, and logical reasoning to the interpretation of forensic evidence.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Understand the role of propositions and the case’s contextual information in the interpretation of traces in forensic science |
|---|---|
| 2. | Apply probability theory and Bayesian networks to the evaluation of the observations made on the traces and the results obtained from their analyses |
| 3. | Evaluate the value of traces in a wide range of scenarios encountered in forensic science (including source level and activity level propositions, database hits, and multiple traces) |
| 4. | Explain the value of the evidence in verbal and written forms without committing any logical fallacies |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of following course intended learning outcomes:
- Critically engage with the appraisal and application of advanced knowledge, technical skills and research principles relevant to professional practice in forensic science. (1.1)
- Assess, argue for, and conduct appropriate approaches to complex forensic science problems through investigation, analysis and independent research in a relevant professional context. (2.1)
- Communicate complex ideas and justifications in a rigorous and professional manner using a variety of methods (oral, written, visual) to discipline experts, scientists, stakeholders, and the general public. (5.1)
Contribution to the development of graduate attributes
Forensic Inference and Interpretation is a foundational subject within the forensic science degree program at UTS. Skills learnt in this subject will be applied throughout the remainder of the course and are key to graduate success. Students are introduced to the problem-solving, scientific and communication skills required in a professional context. Upon completion of this subject, students should be able to assign appropriate evidential weight to various types of traces in the context of cases typically encountered in forensic science.
Graduate Attribute 1 – Disciplinary Knowledge
Students will develop a working knowledge of forensic science practice, probabilistic evaluation of traces in the context of their case, and integration of these results in the forensic science laboratory and legal system in the lectures and workshops. Students will learn how to formulate an appropriate pair of propositions based on the case circumstances, assign likelihood ratios for the evidential value of multiple types of traces, and present the weight of evidence in a logical and transparent manner. This will be assessed as part of the online tutorial activities (Assessment Task 1), and Assessment Task 3.
Graduate Attribute 2 – Research, Inquiry and Critical Thinking
Students will investigate case examples in the tutorial activities and engage in group activities to solve trace evaluation problems using knowledge drawn from the lectures, computer labs and from self-directed learning activities. This will be assessed in all three assessment tasks.
Graduate Attribute 5 – Communication
Students will be introduced to the skills required for professional-level communication in forensic science. Students will learn and be assessed on their oral and written communication skills in the presentation of probabilistic information. Students will be able to develop their oral communication skills during the computer labs and workshops where there will be opportunities for them to present their findings and receive informal feedback. Students’ written communication skills will be assessed in Assessment Tasks 2 and 3.
Teaching and learning strategies
This subject will be delivered through online lectures, computer labs, workshops, and independent learning activities.
Lectures
There will be online lecture material each week. These sessions introduce and explain key principles in forensic inference and interpretation and relate them to modern professional practice. Students must read the relevant lecture notes, watch the lecture videos, and study the additional reading material provided on Canvas, as this will prepare the students for active discussion in the workshop. All resources used in the lectures will be available through Canvas before the scheduled workshop classes, allowing students to concentrate on and contribute to the discussion in class. Case studies are integrated extensively throughout the lectures to provide a context for the theory being presented. Each lecture will be accompanied by a short quiz (see Assessment Task 1). The completion of this quiz is mandatory to participate in that week's workshop.
Computer Labs
The computer labs are an essential part of the subject as they will consolidate a student's understanding of concepts related to the treatment and visualization of forensic data. Students will work in groups to solve problems, and will be required to present their results verbally to the class and in written form (see Assessment Task 2). These will be followed by class discussions and oral feedback. More specifically, computer lab activities will include learning and exploring the use of RStudio for solving problems, studying case scenarios, and visualizing data and probabilistic models. Attendance for the computer lab program is compulsory and assessed through a marked assessment task conducted in groups (see Assessment Task 2) and an individual assessment task (see Assessment Task 3). Written/Oral feedback will be given to each group for Assessment Task 2.
Workshops
The workshops are an opportunity for students to engage in in-depth discussions of topics presented in lectures, obtain additional explanations on these topics, and explore the interpretation of evidence in mock case scenarios. In addition, they offer opportunities for questions and clarification of subject material. Attendance is recommended at all workshops to develop a complete understanding of the content. More specifically, workshop activities will include studying case scenarios, assessing expert witness statements, and evaluating forensic results for various types of traces in mock case scenarios.
Independent Learning Activities
Independent learning activities with structured feedback are employed in Forensic Inference and Interpretation, accessed through Canvas. These take the form of self-directed online activities. These online activities accompany the lectures throughout the semester to provide students with learning opportunities, exercises and individual feedback prior to the larger assessment activities. The online activities contain compulsory exercises that are formally assessed (see Assessment Task 1), and contribute to the student’s final grade. After the submission deadline, the student will be able to see which exercises they got right or wrong, and this will give them individual feedback on how well they understood each topic.
Content (topics)
- Inference in forensic science. Introduction to probability calculations, laws of probability, conditional probability, Bayes’ theorem (probabilities), reasoning processes.
- Principles of evidence evaluation. Weight of evidence. Match probability, likelihood ratio, Bayes’ theorem (odds), working with Bayes’ theorem, reporting a likelihood ratio. Transposed conditional, prosecutor’s fallacy, defence attorney’s fallacy, uniqueness fallacy.
- Principles for formulating propositions. Hierarchy of propositions, propositions vs. explanations, defining the relevant population.
- Introduction to Bayesian networks (BNs). Definition of BNs, propagation of uncertainty in a BN, fundamental types of connections. Source level evaluation.
- Evidence assessment at the activity level. Likelihood ratios, transfer probabilities, background probabilities, BN model. Evaluation of glass evidence. Evaluation of fibre evidence.
- Evidence assessment at the offense level. Likelihood ratios, relevance of a trace, relationship between offense and source level propositions, combining evidence, BN model.
- Evidence assessment for combining evidence. Likelihood ratio, two-trace problem, database search problem, BN models.
- Case pre-assessment. CAI model.
- Validation of forensic methods. Discriminating power, misleading evidence, performance, calibration, Tippett plots, entropy, empirical cross entropy plots.
Assessment
Assessment task 1: Independent Learning Activities
| Intent: | This assessment task contributes to the following graduate attributes:
|
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2 and 3 This assessment task contributes to the development of course intended learning outcome(s): 1.1 and 2.1 |
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | This assessment task consists of 10 quizzes distributed weekly throughout the semester, starting in Week 1. Each quiz consists of 3-4 MCQ questions. |
| Criteria: | 1. correct choice of reasoning; 2. correct application of knowledge and techniques of forensic inference and interpretation; 3. correct choice of problem solving strategies and procedures; |
Assessment task 2: RStudio Data Analysis
| Intent: | This assessment task contributes to the following graduate attributes: 2. Research, Inquiry and Critical Thinking 5. Communication |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 4 This assessment task contributes to the development of course intended learning outcome(s): 2.1 and 5.1 |
| Type: | Report |
| Groupwork: | Group, group and individually assessed |
| Weight: | 30% |
| Criteria: | Students will be assessed based on:
|
Assessment task 3: Evaluation of Evidence Report
| Intent: | This assessment task contributes to the following graduate attributes: 1. Disciplinary Knowledge 2. Research, Inquiry and Critical Thinking 5. Communication |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): 1, 2 and 4 This assessment task contributes to the development of course intended learning outcome(s): 1.1, 2.1 and 5.1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 40% |
| Criteria: | Students will be assessed based on:
|
Recommended texts
C.G.G. Aitken and F. Taroni. Statistics and the Evaluation of Evidence for Forensic Scientists. John Wiley & Sons, Chichester, 2nd edition, 2004, ISBN 0-470-84367-5.
J.M. Curran. Introduction to Data Analysis with R for Forensic Scientists. CRC Press, Boca Raton, 2011, ISBN 978-1-4200-8826-7.
B. Robertson, G.A. Vignaux, C.E.H. Berger. Interpreting Evidence. John Wiley & Sons, Chichester, 2nd edition, 2016, ISBN 9781118492482.
D.V. Lindley. Understanding Uncertainty. John Wiley & Sons, Chichester, 2nd edition, 2013, ISBN 9781118650127.
F. Taroni, C.G.G. Aitken, P. Garbolino, A. Biedermann. Bayesian Networks and Probabilistic Inference in Forensic Science. John Wiley & Sons, Chichester, 2006, ISBN 9780470091739.
F. Taroni, S. Bozza, A. Biedermann, P. Garbolino, C.G.G. Aitken. Data Analysis in Forensic Science: A Bayesian Decision Perspective. John Wiley & Sons, Chichester, 2010, ISBN 9780470998359.
J.S. Buckleton, J.-A. Bright, D. Taylor. Forensic DNA Evidence Interpretation. CRC Press, Boca Raton, 2nd edition, 2016, ISBN 9781482258899.