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COMP3425 Data Mining
创建日期：2024-04-27 17:29:38
标签： COMP3425
Data Mining

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COMP3425 Data Mining 


Assignment 1
Maximum marks 100
Minimum to pass
hurdle

30
Length
Maximum of 8 pages excluding cover sheet, bibliography and
appendices.
Layout
A4. At least 11 point type size. Use of typeface, margins and
headings consistent with a professional style.
Submission deadline 9 am, Monday 14 March
Submission mode Electronic, PDF via Wattle, file-name includes u-number
Estimated time 15 hours
Penalty for lateness 100% after the deadline has passed
First posted: 21 Feb, 9am
Last modified: 21 Feb, 9am
Questions to: Wattle Discussion Forum

This assignment specification may be updated to reflect clarifications and modifications
after it is first issued.
In this assignment, you are required to submit a single report comprising your answers to
set questions in the form of a single PDF file with a file-name that includes your University
u-number ID. The first page must have a clearly identified title and author, identified by
both name and university u-number. You may also attach supporting information
(appendices) in the same PDF file. Appendices will not be marked but may be treated as
supporting information to your answers.
This is a single-person assignment and should be completed on your own. Make certain you
carefully reference all the material that you use. Any material that you wish to quote must
have the source clearly referenced. It is unacceptable to present any portion of another
author's work as your own. Anyone found doing so will be penalised in marks. In addition,
CECS procedures for plagiarism will apply.
It is strongly suggested that you start working on the assignment right away. You can submit
as many times as you wish. Only the most recent submission at the due date will be
assessed.

Task
The Australian Computer Society Code of Professional Conduct 2014 is expected to be
applied by all Computing Professionals in Australia. It sets out six values but stresses the
primacy of the public interest as the overriding value. In 2017, the US Branch of the
Association for Computing Machinery (ACM), recognizing the ubiquity and far-reaching
impact of algorithms in daily lives, issued a Statement on Algorithmic Transparency and
Accountability incorporating seven Principles designed to address potential harmful social
discrimination due to bias. In 2018, the Australian Government Office of the Australian
Information Commissioner released the Guide to Data Analytics and the Australian Privacy
Principles (APP). These three documents are provided with this assignment specification.
You must also read the paper, Clarke R. (2018), “Guidelines for the Responsible Application
of Data Analytics” Computer Law & Security Review 34, 3 (Jul-Aug 2018), that is provided
with this assignment specification and hereafter referred to as the Guidelines. You must also
read the paper, Du, Liu and Hu, (2020) “Techniques for Interpretable Machine Learning”,
Communications of the ACM 63(1) that is also provided with the assignment.
You are to consider the application of the ACS code of conduct, the 7 US ACM Principles,
Clarke’s Guidelines and Du et al’s Techniques to the following fictitious ad targeting
scenario. You may also use the APP guide, where it is helpful.
Ad Targeting Scenario (from Clarke R. (2016) “Big Data, Big Risks”, Information Systems
Journal 26, 1 (January 2016) 77-90, PrePrint at http://www.rogerclarke.com/EC/BDBR.html

A social media service-provider accumulates a vast amount of social transaction data, and some
economic transaction data, through activity on its own sites and those of strategic partners. It applies
complex data analytics techniques to this data to infer attributes of individual digital personae. It
projects third-party ads and its own promotional materials based on the inferred attributes of online
identities and the characteristics of the material being projected.
The 'brute force' nature of the data consolidation and analysis means that no account is taken of the
incidence of partial identities, conflated identities, obfuscated identities, and imaginary, fanciful,
falsified and fraudulent profiles. This results in mis-placement of a significant proportion of ads, to
the detriment mostly of advertisers, but to some extent also of individual consumers. It is challenging
to conduct audits of ad-targeting effectiveness, and hence advertisers remain unaware of the low
quality of the data and of the inferences. This approach to business is undermined by inappropriate
content appearing on childrens' screens, and gambling and alcohol ads seen by partners in the
browser-windows of nominally reformed gamblers and drinkers.
You must answer the following questions, clearly indicating which question you are
answering within your submission. The page lengths suggested for each question here are
for guidance only; the given page length limit for the overall assignment is mandatory.
Question 1. (1 page) Consider the ACS code of conduct. For each of the six values, taking
account of any relevant sub-parts, discuss whether the value was demonstrated in the
scenario and to what extent. If you assess any value as largely irrelevant to the scenario,
then a very brief reason for this assessment is sufficient.
Question 2. (1/2 page) Consider the 7 US ACM Principles. Looking closely at Principle 1,
Awareness, discuss how this principle is applied (or not) in the scenario and identify any
“potential harm” that might have ensued.
Question 3. (2 pages) Consider the numbered guidelines in Table 2 of Clarke’s Guidelines
for the responsible application of data analytics. From every segment (1 General, 2 Data
Acquisition, 3 Data analysis, and 4 Use of the Inferences) choose one guideline that you
consider would have been applied in the scenario. Its application may not be explicit in the
scenario description, but it should be relevant and important to the scenario and you can
argue that it was applied properly and therefore did not contribute to the negative
consequences of the scenario. Explain its role in the scenario including how it would have
contributed to positive outcomes. Justify why it is more relevant than every one of the other
guidelines that you consider would have been applied in the same segment. Argue how it is
more or less relevant than any guidelines in the same segment that you consider may have
been disregarded in the scenario. Be careful to consider the intention of the guidelines
rather than an overly literal interpretation; you may rephrase the chosen guideline for the
scenario context where beneficial. For further explanation of this point, see Section 3 in
Clarke’s Guidelines.
Question 4. (1 page) (a) Choose one, numbered guideline (e.g. guideline 3.3) in Table 2 of
the Guidelines that you consider to have been disregarded in the scenario. You may choose
any guideline that you did not choose for Question 3. Discuss how the failure to consider
the guideline could have contributed to the negative outcome of the scenario. (b) In
addition, identify any other potential consequences that could have occurred due to the
failure to consider that same guideline. For this purpose, the consequences you identify are
not necessarily explicit within the scenario description. You might find it helpful to think of
this activity as contributing to a risk assessment process prior to your hypothetical
involvement in the analysis work of the scenario.
Question 5. (1 page) Consider the paper by Du et al, Techniques for Interpretable Machine
Learning. Discuss whether and how intrinsic and post-hoc interpretability techniques could
be applied to the scenario and what benefits could ensue.
General Comments
An abstract or executive summary is not required. A cover sheet is optional and does not
contribute to the page count. No particular layout is specified, but you should follow a
professional style and use no smaller than 11 point typeface and stay within the maximum
specified page count. Page margins, heading sizes, paragraph breaks and so forth are not
specified but a professional style must be maintained. Text beyond the page limit or word
count limit will be treated as non-existent. Appendices may be used and do not contribute
to the page count, but appendices might be only quickly scanned or used for reference and
will not be specifically marked.
You must properly attribute the source documents provided for your assignment (but not
this assignment specification itself) and any other reference materials you choose to use.
You are not required to use additional materials. No particular referencing style is
required. However, you are expected to reference conventionally, conveniently, and
consistently. Your references should be sufficient to unambiguously identify the source, to
describe the nature of the source, and also to retrieve the source in online and (if possible)
traditional publisher formats.
An assessment rubric is provided. The rubric will be used to mark your assignment. You are
advised to use it to supplement your understanding of what is expected for the
assignment and to direct your effort towards the most rewarding parts of the work.
Your assignment submission will be treated confidentially, but it will be available to ANU
staff involved in the course for the purposes of marking.
Assessment Rubric
This rubric will be used to mark your assignment. You are advised to use it to supplement your understanding of what is expected for the assignment and to
direct your effort towards the most rewarding parts of the work. Your assignment will be marked out of 100, and marks will be scaled back to contribute to
the defined weighting for assessment of the course.
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 EARN 5 FE 825 IERG 5130 STAT 8178 EE161 ADS2 G54FUZ CS326 113B ECN 226 QBUS6860 ELEC3202 COMP9334 MGT11B MET CS 777 STA238 00099F Python SMM306 CMT206 EC116 SP MAST90084 CS480/680 BCPM0091 CMPT 300 ECON2209 FY20 STAT 432 CVEN9625 MATH1712 CMP9771M CMP3750M CIS 325 AI506 MAT344H1S MGT7180 PSTAT 160B CSYS5010 1D STAT 453/558 ME152B STA304H1F CSCI-128 ME428 CO3002 FINE 7640 COMP3234 ECSE 223 EE497 – 3D MATH5905 ECS648U ENG5009 MATH10242 EC665 A CPI221 ISOM5220 MACE40402 MGT B456F COMP9315 AFM 113 STAT 778 CS 564 MA231 CS 230 CS 124 COMP5511 CSC317 ITEC1010 ELEC207 INFT 3019 7CCMFM06 EE 161 5G ECLT5810 MATH 7349 CS526 O2 COMP90054 ELE00063M ACCT5034 ARC1015 CSCI 40 CSCI E40 FIN 524B CS 462 389COM CMT309 MFIN703 COMP4250 STA304 STA304H1S STA 1003 FIN 448 BUFN 732 CS520 CVEN3701 ECMT6006 PHAS0100 CSIT314 MDIA 5028 CSCI 2134 MAT 451 CMPT 459 STAT 457 INFO20003 COMP4141 IELE8066 F540 ECM9 CSE 6321 FIN 430 MATH3911 BFC5916 COMP1011 EEE 6209 STATS 240 CW3 COMP206 COMP1003 4331W ISE 530 CMPT 295 MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 MGMTMFE 405 25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 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