Impact of Observing Unethical Behavior

This assignment delves into the psychological effects of witnessing unethical behavior on an individual's propensity to engage in dishonest actions. It examines three key theories: the perceived probability of getting caught, the saliency hypothesis regarding ethical considerations, and the influence of social norms. The text analyzes how observing others' dishonesty can alter one's perception of risk, ethical standards, and societal expectations, ultimately shaping their own moral compass.

Running Head: INFORMATION TECHNOLOGY ETHICS
Information Technology Ethics
Name of the Student
Name of the University

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Case Study 1
Part 1 – Question 1
Q1. What going on?
Jean uses segments of code from both her co-worker and the commercial software, but
does not tell anyone or mention it in the documentation. She completes the project and turns it in
a day ahead of time. This activity is not ethical and it directly violates the theory of virtue.
Q2. What are the facts?
The facts are as follows:
 Jean, a statistical database programmer, is trying to write a large statistical
program needed by her company.
 Her manager, not recognising the complexity of the problem, wants the job
completed within the next few days.
 Not knowing how to solve the problems, Jean remembers that a coworker had
given her source listings from his current work and from an early version of a
commercial software package developed at another company.
 On studying these programs, she sees two areas of code which could be directly
incorporated into her own program. She uses segments of code from both her co-
worker and the commercial software, but does not tell anyone or mention it in the
documentation.

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Q3. What are the issues?
The main issue is that Jean steals her co-workers’ work without acknowledging them or
even letting them know.
Q4. Who is affected?
The co-workers are affected in the long run.
Q5. What are ethical issues and implications?
Using other’s work without acknowledging them is strictly against the ethical theory of
virtue. Jean deliberately breaks it in order to complete her work.
Q6. What can be done about it?
Severe punishments (like penalties, suspensions and others) can be imposed in the case
someone steals others’ works.
Q7. What are the options?
The main option is to encourage team venture rather than individual so that none’s work
is stolen.
Q8. Which option is the best and why?
The best option is the promotion of group ventures in a project rather than blaming one
particular member.
Part 1 – Question 2
This case study directly violates the theory of virtue. Virtue Ethics (or Virtue Theory) is
an approach to Ethics that emphasizes an individual's character as the key element of ethical

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thinking, rather than rules about the acts themselves (Deontology) or
their consequences (Consequentialism). This is violated in this case study.
Case Study 2
Part 1 – Question 1
Q1. What going on?
Company X has just signed a business agreement with Company Y, which entitles both
of them to access each other clients’ records. Mr. Faisal, a software programmer at Company Z,
was assigned the task of developing a software program that handles the access and retrieval of
records from each Company’s database system into the other. Faisal told his manager about the
problem he encountered and explained its significance. The manager's response was, "That's not
our problem; let's just be sure that our software functions properly." This violates the ethical
theory of deontology.
Q2. What are the facts?
The facts are as follows.
 Mr. Faisal, a software programmer at Company Z, was assigned the task of
developing a software program that handles the access and retrieval of records
from each Company’s database system into the other.
 A first run of the software on real data indicated that the work was well within the
state of the art, and no difficulties were found or anticipated.

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 Several weeks later and during a normal test on the software developed, Faisal
discovered a serious ‘security hole’ in the database system of Company Y by
which hackers can easily obtain confidential information about clients.
 Faisal told his manager about the problem and explained its significance. The
manager's response was, "That's not our problem; let's just be sure that our
software functions properly."
Q3. What are the issues?
Faisal discovered a serious ‘security hole’ in the database system of Company Y by
which hackers can easily obtain confidential information about clients. He was convinced that
while the software he developed could correctly accomplish the task, the code in Company Y’s
database system could not be trusted as the security hole posed a threat even on Company X’s
database system. However, the ethical issue is that the manager wants to hide this issue and let
Company Y find it themselves.
Q4. Who is affected?
In the long run, company X is affected.
Q5. What are ethical issues and implications?
In moral philosophy, deontological ethics or deontology is the normative ethical position
that judges the morality of an action based on rules. It is sometimes described as "duty-" or
"obligation-" or "rule-" based ethics, because rules "bind you to your duty".
Q6. What can be done about it?
In order to solve this, the manager should take active initiative to solve the problem from
Company X’s end rather than leaving it for Company Y.

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Q7. What are the options?
There are no other options as the changes must be done before the database whole is
exploited by external malwares.
Q8. Which option is the best and why?
This is the best option because not only it follows Deontology theory but also addresses
the ethical aspect of the issue.
Part 1 – Question 2
This case study violates the ethical theory of deontology. In moral
philosophy, deontological ethics or deontology is the normative ethical position that judges the
morality of an action based on rules. It is sometimes described as "duty-" or "obligation-" or
"rule-" based ethics, because rules "bind you to your duty". However, this is not fulfilled in this
case study.
Case Study 3
Part 1 – Question 1
Q1. What going on?
The supervisor of the office wants to use pirated / unauthorized version of the software
without paying the developer.
Q2. What are the facts?
The facts are as follows.

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 The objective is to enable ABC to keep better track of their inventory, to be more
responsive to changes in customer demand, and to adopt a “just in time” strategy
to reduce inventory.
 There is a software available but it is very expensive.
 The supervisor says he will use the version he already has for ABC although it
violates the agreement policy of the software.
Q3. What are the issues?
The main issue with this is that it violates the agreement policy of the software.
Q4. Who is affected?
The developer of the software is affected.
Q5. What are ethical issues and implications?
The main ethical issue is that it violates the agreement policy and the developer is
affected in terms of revenue.
Q6. What can be done about it?
Original version of the software should be bought with proper license.
Q7. What are the options?
Using one version / pirated version of the software should be stopped and a new original
version should be bought and used. This helps gaining revenue for the developers and also meets
the agreement policies.

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Q8. Which option is the best and why?
The best option is to buy a new version of the chosen software even though it is
expensive or else buy a cheaper alternative software.
Part 1 – Question 2
This case study violates the ethical theory of contract. The agreement policy is a type of
contract that is agreed when the software is bought. However, this agreement policy does not
allow the user to use it in multiple systems. Hence, this case violates the theory.
Essay Topic 1
Enterprise software refers to software that businesses use to run their day-today activities
such as finance, sales, human resources, manufacturing, shipping, and procurement. It is
typically purchased by companies as off-the-shelf software, customized and configured to meet
their business needs, and made available to their employees. Enterprise software provides
visibility to executives regarding the health of their organizations and enables them to make
course corrections as needed. Finding and removing software faults is the classic strategy for
dealing with them. Fixing bugs in the operational phase is considerably more expensive than
doing so in the development or testing phase. Therefore, engineers expend much effort on
detecting and removing bugs during software development via both dynamic software tests and
static techniques like code reviews and walkthroughs. Systematically conducted unit and system
tests play an important role in revealing faults that lead to failures during software execution.
However, diagnosing and isolating the underlying fault responsible for an observed failure
becomes difficult if the failure cannot be reproduced. Software testing is, therefore, mainly
suitable for dealing with faults that consistently manifest under well-defined conditions. To

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explain this phenomenon, it’s useful to take a closer look at how the static fault in the software is
connected to the dynamic failure occurrence. Usually, activating a fault by executing the part of
the software where it’s located doesn’t immediately cause a failure. Rather, it produces an
internal condition in the system that deviates from the correct internal condition—referred to as
an error— even though the user might not perceive this discrepancy. An error can develop into
further errors before a failure finally occurs. This functional chain between errors and failure is
called error propagation. For example, a fault in an algorithm’s implementation can lead to an
erroneous computation for specific values of a program variable—a case of fault activation
causing an error. The software can use this incorrect result internally for further calculations, in
which case the error propagation leads to additional errors. A failure occurs only when the
system uses one of these incorrect calculations in a way that influences a perceivable system
behavior, or when error propagation causes a failure occurrence. Based on the relationships
between faults, errors, and failures, researchers can offer two explanations as to why software
may behave differently under apparently identical conditions. First, if there’s a long delay
between the fault activation and the final failure occurrence—for example, traversing several
different error states in the error propagation—then it’s difficult to identify the user actions that
actually activated the fault and caused the failure. Simply repeating the steps carried out a short
time before the failure occurrence might not lead to its reproduction. Second, other elements of
the software system—such as the operating system, other applications, or the hardware—can
influence a fault’s behavior in a specific application. researchers refer to the set of these elements
as the application’s system-internal environment. For example, inadequate synchronization in
multithreaded software can give rise to race conditions, in which the program behavior depends
on the relative timing of the threads the operating system schedules. Since a failure only occurs if

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the operating system schedules the threads in a specific order that the programmers didn’t
foresee, troubleshooters find it difficult to reproduce such failures and isolate the underlying
faults. There are various reasons for discussing the issue of ethics within a software engineering
context. By participating in a software development process, software engineers can influence
the final product, namely the software itself, in different ways including those that may be
contrary to public interest. In other words, they could engage in an unethical behavior,
inadvertently or deliberately. This could lead to personal harm, and potentially result in loss of
confidence in software and loss of trust in organizations that own them. This can adversely affect
the acceptance of software as a useful product, question the credibility of software engineering as
a profession, lead to legal implications, and impact the bottom line of the software industry at-
large. Since a software can either be a benefit or a hazard to its potential users, the issue of ethics
in its engineering arises. Software failures that have led to loss of human life, rendered computer
systems unusable, led to financial collapse, or caused major inconveniences are grim reminders
of that. In this article, researchers discuss the issue of ethics from the viewpoint of software
product quality considerations in practice. There is an apparent symbiosis between ethics and
quality. For example, the causes of the aforementioned failures were attributed to violations of
one or more quality attributes such as reliability, safety, and so forth, and/or to lack of proper
validation/verification of these. Indeed, in the Software Engineering Body of Knowledge
(SWEBOK), ethics has been placed within the software quality “knowledge area.” The issue of
information technology in general, and the role of quality in software development in particular,
have been addressed in. Moreover, software quality is viewed as an ethical issue from a
philosophical perspective. However, these efforts are limited by one or more of the following
issues: quality and ethics are often viewed as a tautology, treatment of software quality is at a

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very high level and often as a single entity, and there is lack of specific guidance for
improvement of software quality within the domain of software ethics. One way to enforce
ethical standards in a software project is by explicitly documenting the ethical expectations from
stakeholders such as via a code of ethics. The Software Engineering Code of Ethics and
Professional Practice (SECEPP) is a recommendation of the ACM/IEEE-CS Joint Task Force on
Software Engineering Ethics and Professional Practices. SECEPP puts forth eight categories of
principles decomposed further into clauses that software engineers should adhere to in teaching
and practicing software engineering. However, these principles and associated clauses suffer
from several issues (expounded in the next section): lack of separation (of concerns), recency,
precision, completeness, reachability (to certain audience), and specificity, which makes their
realization difficult. The relevance of SECEPP for practical purposes has been questioned
(Qureshi, 2001), however the view is largely managerial rather than oriented towards the
software product.
Essay Topic 2
Every programmer has a characteristic style, ranging from preferences about identifier
naming to preferences about object relationships and design patterns. Coding conventions define
a consistent syntactic style, fostering readability and hence maintainability. When collaborating,
programmers strive to obey a project’s coding conventions. However, one third of reviews of
changes contain feedback about coding conventions, indicating that programmers do not always
follow them and that project members care deeply about adherence. Unfortunately, programmers
are often unaware of coding conventions because inferring them requires a global view, one that
aggregates the many local decisions programmers make and identifies emergent consensus on

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style. To program is to make a series of choices, ranging from design decisions — like how to
decompose a problem into functions — to the choice of identifier names and how to format the
code. While local and syntactic, the latter are important: names connect program source to its
problem domain; formatting decisions usually capture control flow. Together, naming and
formatting decisions determine the readability of a program’s source code, increasing a
codebase’s portability, its accessibility to newcomers, its reliability, and its maintainability.
Apple’s recent, infamous bug in its handling of SSL certificates exemplifies the impact that
formatting can have on reliability. Maintainability is especially important since developers spend
the majority (80%) of their time maintaining code. A convention is “an equilibrium that
everyone expects in interactions that have more than one equilibrium”. For us, coding
conventions arise out of the collision of the stylistic choices of programmers. A coding
convention is a syntactic restriction not imposed by a programming language’s grammar.
Nonetheless, these choices are important enough that they are enforced by software teams.
Indeed, our investigations indicate that developers enforce such coding conventions rigorously,
with roughly one third of code reviews containing feedback about following them. Like the rules
of society at large, coding conventions fall into two broad categories: laws, explicitly stated and
enforced rules, and mores, unspoken common practice that emerges spontaneously. Mores pose a
particular challenge: because they arise spontaneously from emergent consensus, they are
inherently difficult to codify into a fixed set of rules, so rule-based formatters cannot enforce
them, and even programmers themselves have difficulty adhering to all of the implicit mores of a
codebase. Furthermore, popular code changes constantly, and these changes necessarily embody
stylistic decisions, sometimes generating new conventions and sometimes changing existing
ones. To address this, researchers introduce the coding convention inference problem, the

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problem of automatically learning the coding conventions consistently used in a body of source
code. Conventions are pervasive in software, ranging from preferences about identifier names to
preferences about class layout, object relationships, and design patterns. In this paper,
researchers focus as a first step on local, syntactic conventions, namely, identifier naming and
formatting. These are particularly active topics of concern among developers, for example,
almost one quarter of the code reviews that researchers examined contained suggestions about
naming. researchers introduce NATURALIZE, a framework that solves the coding convention
inference problem for local conventions, offering suggestions to increase the stylistic consistency
of a codebase. NATURALIZE can also be applied to infer rules for existing rule-based
formatters. NATURALIZE is descriptive, not prescriptive1 : it learns what programmers actually
do. When a codebase does not reflect consensus on a convention, NATURALIZE recommends
nothing, because it has not learned anything with sufficient confidence to make
recommendations. The naturalness insight is that most short code utterances, like natural
language utterances, are simple and repetitive. Large corpus statistical inference can discover and
exploit this naturalness to improve developer productivity and code robustness. researchers show
that coding conventions are natural in this sense. Learning from local context allows
NATURALIZE to learn syntactic restrictions, or sub-grammars, on identifier names like
camelcase or underscore, and to unify names used in similar contexts, which rule-based code
formatters simply cannot do. Intuitively, NATURALIZE works by identifying identifier names
or formatting choices that are surprising according to a probability distribution over code text.
When surprised, NATURALIZE determines if it is sufficiently confident to suggest a renaming
or reformatting that is less surprising; it unifies the surprising choice with one that is preferred in
similar contexts elsewhere in its training set. NATURALIZE is not automatic; it assists a

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developer, since its suggestions, both renaming and even formatting, as in Python or Apple’s
aforementioned SSL bug, are potentially semantically disruptive and must be considered and
approved. NATURALIZE’s suggestions enable a range of new tools to improve developer
productivity and code quality: 1) A pre-commit script that rejects commits that excessively
disrupt a codebase’s conventions; 2) A tool that converts the inferred conventions into rules for
use by a code formatter; 3) An Eclipse plugin that a developer can use to check whether her
changes are unconventional; and 4) A style profiler that highlights the stylistic inconsistencies of
a code snippet for a code reviewer. NATURALIZE draws upon a rich body of tools from
statistical natural language processing (NLP), but applies these techniques in a different. NLP
focuses on understanding and generating language, but does not ordinarily consider the problem
of improving existing text. The closest analog is spelling correction, but that problem is easier
because researchers have strong prior knowledge about common types of spelling mistakes. An
important conceptual dimension of our suggestion problems also sets our work apart from
mainstream NLP. In code, rare names often usefully signify unusual functionality, and need to be
preserved. researchers call this the sympathetic uniqueness principle (SUP): unusual names
should be preserved when they appear in unusual contexts. researchers achieve this by exploiting
a special token UNK that is often used to represent rare words that do not appear in the training
set. Our method incorporates SUP through a clean, straightforward modification to the handling
of UNK. Because of the Zipfian nature of language, UNK appears in unusual contexts and
identifies unusual tokens that should be preserved. Section 4 demonstrates the effectiveness of
this method at preserving such names. Additionally, handling formatting requires a simple, but
novel, method of encoding formatting. As NATURALIZE detects identifiers that violate code
conventions and assists in renaming, the most common refactoring, it is the first tool researchers

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are aware of that uses NLP techniques to aid refactoring. The techniques that underlie
NATURALIZE are language independent and require only identifying identifiers, keywords, and
operators, a much easier task than specifying grammatical structure. Thus, NATURALIZE is
well-positioned to be useful for domain-specific or esoteric languages for which no convention
enforcing tools exist or the increasing number of multi-language software projects such as web
applications that intermix Java, css, html, and JavaScript.
Essay Topic 3
It is almost impossible to open a newspaper or turn on a television without being exposed
to a report of dishonest behavior of one type or another. Names such as Enron, Tyco, and Arthur
Andersen provide extreme examples; other examples include cheating on taxes, insurance fraud,
employee theft, academic dishonesty, athletes’ use of illegal drugs, and of course illegal
downloading of software and digital content. Given so many first- and second-hand encounters
with unethical behavior, one important question that comes to mind concerns the effect of such
exposure on otherwise honest individuals. Do they tend to start engaging in unethical behavior?
In the current work, we explored this very question by examining the conditions under which
exposure to the unethical behavior of another person increases or decreases individuals’
dishonesty. The unethical behavior of other individuals can influence observers’ behavior in (at
least) three possible ways. First, when exposed to the dishonesty of others, individuals may
change their estimate of the likelihood of being caught cheating (e.g., a student who sees a peer
cheating on an exam and getting away with it may change his or her estimation of the probability
of being caught in the act). Together with the amount to be gained from cheating and the
expected punishment, the likelihood of being caught cheating is a central input in the rational

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crime theory. In this rational framework, the individual engages in a cost-benefit calculation that
leads to the ultimate decision about dishonesty. As a consequence of such cost-benefit analysis,
any change in the estimation of the likelihood of being caught cheating can influence the
magnitude of dishonesty an individual chooses to engage in (e.g., the student who sees a peer
cheating on an exam and getting away with it changes his or her estimation of the probability of
being caught in the act and is thus more likely to cheat). A second way in which observing
others’ behavior may change one’s own dishonesty concerns the saliency of ethicality at the
moment one is considering a particular behavior. Previous research has shown that when the
categorization of a particular behavior is not clear-cut, people can, and in fact often do,
categorize their own actions in positive terms, avoiding negative updating to their moral self-
image. However, some researchers found that drawing people’s attention to moral standards
could reduce dishonest behaviors. For example, after being asked to recall the Ten
Commandments, participants who were given the opportunity to cheat, and gain financially from
this action, did not cheat at all; in contrast, participants who had the same opportunity to cheat
but had not been given the moral reminder cheated substantially. These results suggest that when
unethical behavior is made salient, people may pay greater attention to their own moral standards
and categorize the ethicality of their own behavior more rigidly. Such momentary fluctuations in
moral standards are also evident in a study, where it was found that priming participants to
believe in determinism (e.g., by making them read statements endorsing determinism) led to
higher levels of dishonesty than inducing participants to believe in free will. The saliency
hypothesis suggests that when people observe someone behaving dishonestly (e.g., when they
read about a new corruption scheme), the saliency of this act increases, making them pay
attention to honesty and to their own standards of honesty, and, as a consequence, decreasing

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their tendency to engage in dishonest acts. A third possible influence of observing the
unethicality of another person is that it simply changes one’s understanding of the social norms
related to dishonesty. Some researchers defined two types of social norms: descriptive norms,
which specify what most people do in a particular situation, and injunctive norms, which specify
the particular behaviors that most people approve or disapprove of. According to norm-focus
theory the social context determines which of these two types of norms people attend to at a
particular time and how these norms will impinge on an individual’s immediate behavior. For
example, some researchers had a confederate litter in the environment in front of some
participants or simply walk through the environment in front of others. Participants who saw the
confederate litter subsequently littered more than those who did not see the confederate litter if
the environment was clean, but this effect was reversed when the environment was dirty. This
kind of social learning by observing other people’s behavior was also demonstrated in Bandura’s
classic studies, in which children exposed to an aggressive model reproduced considerably more
aggressive behaviors toward a Bobo doll than did children who were not exposed to the
aggressive model. Moreover, children reproduced more aggressive behaviors when an adult did
not comment on the aggressive model’s actions (or when an adult was not present in the room)
than when the adult disapproved of those actions using negative comments. Children might have
interpreted the lack of evaluative comments on the model’s aggressive behavior and the absence
of an adult in the room as signs of permission, through social norms. The social-norms account
implies another important factor that might influence the degree to which people are affected by
the unethical behavior of others around them: the degree to which they identify with those others.
The idea is that when the identification is strong, the behaviors of others will have a larger
influence on observers’ social norms. Field evidence for this idea was obtained in a large-scale

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survey of Australian citizens, which found that the presence of social norms elicited consistent
behavior (i.e., tax compliance), but only when respondents identified with the group to which the
norms were attributed. These findings can be explained by social-identity theory, according to
which group members tend to use their own group to maintain or enhance a positive social
identity and self-esteem, and as a consequence are motivated to conform with norms that provide
them with an in-group identity, rather than an out-group one.