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ImpactMojoDigital Ethics 101www.impactmojo.in
ImpactMojo 101 Series · Free Forever
Digital
Ethics
101
Deploying Technology Responsibly — a Foundational Course on Data, AI & Digital Rights for Development Practitioners in South Asia
Research-BackedSouth Asia Focus100 SlidesFree Access
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Agenda
What We Cover
01
Why Digital Ethics Matters
Slides 3–11
02
Data Privacy & Protection
Slides 12–20
03
Algorithmic Bias & Fairness
Slides 21–29
04
AI in Development
Slides 30–38
05
Digital Identity & Inclusion
Slides 39–47
06
Surveillance, Power & Consent
Slides 48–56
07
The Digital Divide
Slides 57–65
08
Platform Power & Data Colonialism
Slides 66–74
09
Misinformation & Online Harms
Slides 75–83
10
Responsible Design & Principles
Slides 84–91
11
Governance, Regulation & Practice
Slides 92–99
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01
Section One
Why Digital Ethics Matters
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The Promise
Technology entered development with a promise
Mobile money, biometric IDs, telemedicine, e-governance and AI have reached deep into the lives of the poor in South Asia — faster than almost anywhere on earth. The promise was real: leapfrog broken systems, cut leakage, deliver at scale.
But the same systems that include millions can exclude millions — and at this scale, a design flaw is not a bug, it is a policy that touches a billion people.
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Definition
What we mean by digital ethics
Digital ethics
The study of how digital technologies — data, algorithms, platforms, AI — should be designed, deployed and governed so they respect rights, dignity and fairness, especially for the people with least power to refuse them.
It is not a compliance checkbox bolted on at the end. It is a set of questions asked from the first design meeting: who benefits, who is harmed, who can opt out, and who decides?
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The Clash
'Move fast and break things' meets vulnerable people
Move fast and break things.
— an early Silicon Valley motto
When the 'things' you break are someone's ration entitlement, pension, or right to be seen by the state, the cost of a failed experiment is not lost revenue — it is a missed meal.
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Tech4Dev
The double edge of every deployment
Promise
  • Reach the last mile cheaply
  • Cut leakage and ghost beneficiaries
  • Real-time monitoring of services
  • Give people a digital voice
Peril
  • Exclude those who cannot authenticate
  • Surveil the poor as a condition of aid
  • Automate and hide existing bias
  • Extract data, return little value
Neither column is the whole truth. Ethics is the discipline of holding both at once.
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Scale
Why scale changes the ethics
A flawed clinic harms a village. A flawed national platform harms a nation. Digital systems concentrate decisions — one ruleset, one database, one model — so errors no longer cancel out; they replicate.
1 ruleset
decides eligibility for hundreds of millions
1 outage
can freeze entitlements across whole states
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Who Is Affected
Practitioners sit on both sides
You may deploy digital tools — a survey app, a beneficiary database, a chatbot. You are also affected by them — the platforms you depend on, the IDs your participants must carry.
This course is for both roles: to build more carefully, and to push back on systems that harm the communities you serve.
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The Core Questions
Five questions to ask of any digital system
  • Who benefits — and who bears the risk?
  • Who is excluded by the design, and how loudly do they fail?
  • What data is collected, and could it be used against people?
  • Who decides — and can an affected person contest a decision?
  • What if it breaks — is there a human fallback?
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Roadmap
How this course is built
The harms
  • Privacy, bias and AI
  • Identity and surveillance
  • The divide and platform power
  • Misinformation and online harm
The response
  • Responsible design principles
  • Privacy and ethics by design
  • Governance and regulation
  • A practical org checklist
Throughout, the examples are South Asian — Aadhaar, the DPDP Act, WhatsApp, the systems you actually meet at work.
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02
Section Two
Data Privacy & Protection
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Foundations
What counts as personal data
Personal data
Any information that relates to an identified or identifiable individual — name, Aadhaar number, phone, location, biometric, health record, even a combination of seemingly harmless fields.
'It's just a phone number' is how most leaks begin. Personal data is anything that can be traced back to a person, alone or in combination.
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Why Privacy
Privacy is not secrecy — it is control
Privacy is not having something to hide. It is the power to decide who knows what about you, and to live without being watched, profiled or sorted by default.
Arguing that you don't care about privacy because you have nothing to hide is like saying you don't care about free speech because you have nothing to say.
— Edward Snowden
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Landmark
Puttaswamy (2017): privacy is a fundamental right
In K.S. Puttaswamy v. Union of India (2017), a nine-judge bench of the Supreme Court of India held unanimously that the right to privacy is a fundamental right under Article 21 of the Constitution.
9 judges
unanimous Constitution Bench
Puttaswamy, 2017
Art. 21
privacy read into the right to life & liberty
Proportionality
any intrusion must be lawful, necessary & proportionate
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The Test
When can the state intrude on privacy?
Puttaswamy set a proportionality test: a restriction on privacy is valid only if it clears every step. This is the lens for judging any data-collecting programme.
01
LEGALITY: backed by a valid law
→
02
LEGITIMATE AIM: a genuine state goal
→
03
NECESSITY: no less-intrusive way works
→
04
PROPORTIONALITY: benefit outweighs the harm
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Principle
Consent: the floor, not the ceiling
  • Consent must be free — not the price of a basic service
  • Informed — people know what, why, for how long
  • Specific — for a stated purpose, not 'anything forever'
  • Revocable — people can withdraw without penalty
A thumbprint on a form nobody explained, given to receive a pension, is not free or informed consent.
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Principle
Purpose limitation & data minimisation
Purpose limitation
Data collected for one purpose should not be quietly reused for another. Immunisation records are not a recruitment list.
Data minimisation
Collect the least you need. Every extra field is a future liability — a breach risk and a tool that can be turned on people.
The safest data is the data you never collected. Ask of every field: do we truly need this?
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The Law
India's Digital Personal Data Protection Act, 2023
The DPDP Act, 2023 is India's first comprehensive data-protection law. It applies to anyone processing digital personal data — including NGOs, researchers and small organisations.
  • Process data only for a lawful purpose, with consent or a legitimate use
  • Honour rights to access, correction and erasure
  • Notify breaches and the Data Protection Board
  • Stronger safeguards for children's data and processing
'We're a small NGO' is not an exemption. Know your obligations before you collect a single record.
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Re-identification
Anonymisation is harder than deleting names
Removing names does not make data safe. A combination of village + age + caste + occupation can re-identify one person, especially in small areas where few share those traits.
Direct IDs
Name, Aadhaar, phone — remove entirely
Quasi-IDs
Age + place + caste can re-identify — coarsen or aggregate
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03
Section Three
Algorithmic Bias & Fairness
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The Myth
Algorithms are not automatically neutral
It is tempting to think a formula cannot discriminate — the maths just crunches numbers. But an algorithm learns from data produced by an unequal world, and it can faithfully reproduce that inequality at scale.
Bias does not come from 'the maths being racist'. It comes from biased data, biased proxies and biased objectives — all human choices.
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Definition
What algorithmic bias means
Algorithmic bias
A systematic, unfair difference in how an automated system treats different groups — producing outcomes that disadvantage people by gender, caste, religion, region, language or disability.
It is rarely intentional. It is usually inherited — from the data the model was trained on, and the world that data came from.
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Source One
Bias enters through the training data
A model learns patterns from past examples. If history under-served women, recorded fewer female-headed loans, or skipped remote villages, the model learns that those people are 'lower priority' — and repeats it.
The model is a mirror. If the data reflects a world that excluded people, the model will too — only faster and with an air of objectivity.
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Source Two
Proxies smuggle in protected traits
Even if you remove caste, religion or gender from the data, other fields can stand in for them. Pincode can proxy for caste or religion; name can reveal community; occupation can track historical disadvantage.
Dropping the sensitive column does not remove the bias if a proxy remains. The model finds the back door.
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Source Three
Feedback loops make bias self-fulfilling
Biased datapast exclusion recordedModel scoresgroup rated low priorityLess servicefewer loans / visitsNew dataconfirms the bias
Each loop deepens the gap: the group gets less, generates less data, and looks even 'riskier' next time.
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Illustration
When a model fails one group more than another
Illustrative: a 'good' overall model with unequal error rates
Illustrative example, not real data
A 12% overall error can hide a 26% error for one group. Aggregate accuracy is not fairness.
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Many Fairnesses
Fairness is not one thing
There are several mathematical definitions of fairness — equal error rates, equal selection rates, equal outcomes — and they often cannot all hold at once. Choosing one is a value judgement, not a technical default.
So the right question is not 'is it fair?' but 'fair in which sense, for whom, and who decided?'
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Response
Auditing for bias is a practice, not a one-off
  • Disaggregate performance by gender, caste, region, language
  • Test for proxies — can removed traits be reconstructed?
  • Document training data, its gaps and known limitations
  • Re-audit as the system and population change over time
Fairness is not automatic and never finished. It has to be measured, on purpose, again and again.
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04
Section Four
AI in Development
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The Moment
AI arrives in the development sector
From crop advisories and chatbots to diagnostic tools and beneficiary targeting, AI — including large language models — is now being piloted across South Asian programmes. The question is no longer whether, but how responsibly.
AI is a powerful amplifier. It amplifies good design and good data — and it amplifies bias, error and exclusion just as efficiently.
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Opportunities
Where AI can genuinely help
  • Translation: bridge dozens of languages and scripts cheaply
  • Diagnostics: screen X-rays, retina scans, skin conditions where doctors are scarce
  • Targeting: find likely-eligible households for outreach
  • Triage: route calls, summarise field reports, flag urgent cases
Used as an assistant to stretched human workers, AI can extend reach into places services never went.
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Risk One
Opacity: the black-box problem
Black box
A system whose internal reasoning cannot be inspected or explained — you see the input and the output, but not why it decided as it did.
If a model denies someone a benefit and no one can explain why, the person cannot contest it. Opacity quietly removes the right to an explanation — and to appeal.
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Risk Two
Automating exclusion at scale
When an AI system decides who is 'eligible', 'at risk' or 'likely fraudulent', it can lock millions out with no human in the room. The error is invisible to the operator and devastating to the excluded.
A false 'ineligible' is not an abstraction. It is a widow without a pension, a child without a scholarship — harm that compounds silently.
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Risk Three
Hallucination & overtrust
Generative AI can produce confident, fluent answers that are simply wrong. In health, legal or entitlement advice, a plausible falsehood delivered with authority is dangerous.
The risk grows when users assume the machine must know better. Fluency is not accuracy. Always keep a verified human source for high-stakes advice.
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The Safeguard
Human-in-the-loop
01
AI suggests (screen, score, draft)
→
02
Human reviews with context & judgement
→
03
Human decides — and is accountable
→
04
Affected person can question the decision
For any decision that affects rights or entitlements, AI should inform a human, never replace one. Keep a person accountable and reachable.
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Match the Stakes
Not every task needs the same caution
UseStakesGuardrail
Draft a report summaryLowHuman edits before sending
Translate a noticeMediumNative speaker verifies
Screen a medical scanHighClinician confirms every case
Decide benefit eligibilityVery highHuman decides; appeal route open
Calibrate oversight to the harm of being wrong. The higher the stakes, the more human judgement and the easier the appeal.
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Before You Deploy
Questions before piloting any AI
  • What is the harm if it is confidently wrong — and who bears it?
  • Can we explain a decision to the person it affects?
  • Was it tested on people like ours — our languages, our context?
  • Is there a human fallback and a working appeal route?
  • Who is accountable when it fails — and can they be reached?
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05
Section Five
Digital Identity & Inclusion
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Aadhaar
The world's largest biometric ID system
Aadhaar, run by the Unique Identification Authority of India (UIDAI), assigns a 12-digit number linked to fingerprints, iris scans and a photograph. It is the largest biometric identity system ever built.
~1.3 bn
Aadhaar numbers issued (widely reported)
UIDAI, widely reported
UIDAI
the statutory authority that runs it
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The Scale
Enrolment at a scale without precedent
Aadhaar enrolment growth (illustrative trajectory toward ~1.3 bn)
Illustrative trajectory; ~1.3 bn total widely reported by UIDAI
The shape is illustrative; the headline — near-universal adult coverage — is widely reported. Scale this large makes both the gains and the harms enormous.
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The Gains
What digital ID made possible
  • A portable identity for people who had no documents at all
  • Direct benefit transfers into bank accounts, cutting some intermediaries
  • Removal of some duplicate and 'ghost' beneficiaries
  • Easier access to opening bank accounts and SIM cards
For someone previously invisible to the state, a verifiable identity can be genuinely empowering.
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The Harms
Authentication failure becomes exclusion
When entitlements are tied to biometric authentication, anyone the machine cannot read can be turned away. Worn fingerprints from manual labour, poor connectivity, server downtime, or a mismatch can mean no ration today.
Researchers and journalists have documented cases where authentication failures left the elderly and labourers unable to draw the food they were entitled to. Exclusion by error is still exclusion.
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Inclusion vs Exclusion
The same system, two outcomes
DimensionBenefitHarm
IdentityThe undocumented become visibleErrors make the visible disappear
DeliveryFaster transfers, fewer ghostsFailed auth blocks real beneficiaries
MandateOne ID, many servicesFunction creep, no real opt-out
BodiesBiometrics are hard to forgeWorn fingerprints exclude labourers
The lesson is not 'reject digital ID'. It is design for the person the machine fails — because at this scale, even a small failure rate is millions of people.
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Design Fix
Always build a non-digital fallback
Any system where authentication gates a basic entitlement must have a guaranteed manual override — a way to receive your due when the technology fails, without being sent home.
01
Biometric fails
→
02
Try alternative (OTP, face, operator)
→
03
Still fails → manual exception register
→
04
Entitlement delivered — never denied for tech
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Voluntary?
When 'optional' is not really a choice
An ID described as voluntary stops being voluntary if you cannot get a pension, ration, scholarship or SIM without it. Coerced consent is the central tension in mandatory-by-practice digital identity.
Ask of any 'voluntary' system: what happens to someone who says no? If the answer is 'they lose a basic service', it is not voluntary.
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Principle
Inclusion is the test, not enrolment
A digital ID is not successful because a billion people enrolled. It is successful only if the last person — the worn-fingerprint labourer in a low-connectivity village — can still claim what is theirs.
Measure systems by their failure cases, not their headline coverage. The margin is where the ethics lives.
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06
Section Six
Surveillance, Power & Consent
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The Shift
From serving people to watching them
Digital systems built to deliver services also generate detailed records of where people go, what they receive and whom they know. Delivery and surveillance can run on the very same rails.
The question is rarely 'is there a camera?' It is 'who can see this data, link it, and act on it — and can the watched person say no?'
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Function Creep
Data collected for X, used for Y
Function creep
When data gathered for one stated purpose is gradually repurposed for others — without fresh consent, often without the subject's knowledge.
A database built to deliver food can become a tool to track, profile or police the same people. The data outlives the promise made when it was collected.
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Whose Privacy
The surveillance of the poor
Those who depend on the state are watched most. To receive welfare, the poor must hand over biometrics, locations, family details and transaction histories — a level of scrutiny the wealthy never face to access their own money.
Surveillance is distributed unequally: those with the least power are watched the most.
— a recurring finding in surveillance studies
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Conditionality
Welfare conditionality as control
When aid is conditional on monitored behaviour — attendance tracked, accounts linked, movements logged — the price of help becomes constant observation. Dignity is quietly traded for entitlement.
Conditions framed as 'accountability' often land hardest on those least able to comply — and punish poverty rather than relieve it.
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Chilling Effects
Being watched changes behaviour
Chilling effect
When the knowledge or suspicion of being monitored makes people self-censor — avoiding lawful speech, association or services out of fear.
People may skip a clinic, a meeting or a complaint because a record might be kept and used against them. The harm is real even when no one is actually watching.
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Power
Surveillance is about asymmetry
They can see you
Location, transactions, contacts, biometrics — linked across databases into a single profile.
You cannot see them
Who holds the data, what rules apply, how to correct an error or contest a decision — opaque.
The ethical fault line is this asymmetry of visibility and power, not the technology itself.
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Consent Again
Consent under power imbalance
Consent means little when refusal means losing a livelihood or a service. A person desperate for aid cannot freely 'agree' to data terms they could never negotiate.
Where power is unequal, the burden shifts: the system must justify what it takes, rather than the person justify their refusal.
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Safeguards
Guardrails against surveillance creep
  • Purpose-bind data and delete it when the purpose ends
  • Separate service delivery from policing and profiling
  • Minimise linkage across databases by default
  • Independent oversight with teeth, and a real complaint route
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07
Section Seven
The Digital Divide
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Definition
The divide is not one gap but many
Digital divide
The unequal distribution of access to digital devices, connectivity, skills and meaningful use — cutting along gender, income, geography, language and disability.
It is not simply 'has a phone or not'. It runs from owning a device, to affording data, to being able to read the screen, to using it for anything that improves your life.
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Gender
The gender gap in access
Illustrative: mobile & internet access by gender
Illustrative pattern; a real gender gap is widely documented in South Asia
Figures are illustrative, but the pattern is well documented: women in South Asia are markedly less likely to own a phone or use the internet than men.
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Geography
Rural, urban and the connectivity gap
Illustrative: internet use, urban vs rural
Illustrative pattern; an urban-rural gap is widely documented
Coverage maps flatter reality: a tower nearby does not mean an affordable, reliable signal inside a home.
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Other Axes
Income, language and disability
  • Income: data, devices and electricity all cost money the poorest lack
  • Language: most services assume English or one dominant language
  • Literacy: text-heavy interfaces exclude non-readers
  • Disability: apps rarely work with screen-readers or for low vision
These axes stack. A poor, rural, non-literate woman with a disability faces every gap at once.
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Layers
Access, skills, and meaningful use
01
LEVEL 1: Is there a device & signal?
→
02
LEVEL 2: Can they afford to use it?
→
03
LEVEL 3: Do they have the skills & language?
→
04
LEVEL 4: Does it actually improve their life?
Closing only the first gap and declaring victory leaves most people stranded at the higher levels.
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The Trap
The risk of 'digital by default'
When a service goes online-only — applications, grievances, payments — the people on the wrong side of the divide are pushed out of the systems they need most.
Digital-by-default can become digital-or-nothing: efficient for the connected, a wall for everyone else.
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Intermediaries
When access depends on a middleman
Many people reach digital services only through an intermediary — a relative, a shopkeeper, a kiosk operator. That helps, but it means handing over passwords, OTPs and personal data, and trusting someone else with your transactions.
Dependence on intermediaries creates new risks: fees, errors, exclusion of those without a trusted helper, and fresh privacy exposure.
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Design Response
Designing for the unconnected
  • Keep an offline / in-person channel for every essential service
  • Support local languages, voice and icons — not just English text
  • Build for low-end phones, patchy networks and assisted use
  • Measure who is left out, not just who is served
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08
Section Eight
Platform Power & Data Colonialism
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The Gatekeepers
A few platforms shape the digital world
A handful of Big Tech firms run the search engines, app stores, social networks, cloud servers and operating systems that most digital life in South Asia depends on — and most of them are headquartered far away.
When the rails are owned by a few firms, they set the rules: what is visible, what is allowed, what data is taken, and what it costs to take part.
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Concept
Data colonialism
Data colonialism
The large-scale extraction of data from people and communities — often in the Global South — for the benefit of firms elsewhere, echoing older patterns of resource extraction.
The raw material is human life itself: behaviour, location, relationships, attention — mined, processed and monetised far from where it was produced.
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The Flow
Where the value goes
01
Users in the Global South generate data
→
02
Platforms collect & process it abroad
→
03
Profiles, ads & AI models are built
→
04
Profit & control accrue elsewhere
The people who produce the data rarely own it, see the profit, or shape the terms. The value flows one way.
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Extraction
The Global South as a data source
Fast-growing user bases in South Asia are highly valuable: huge markets, rich data, and often weaker bargaining power and regulation. The region supplies users and data, but holds little of the infrastructure or the upside.
Cheap or 'free' services are paid for in data. The product offered for free is usually the user's attention and information.
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Asymmetry
Who captures the value
Platforms gain
  • Ad revenue and market dominance
  • Training data for AI models
  • Lock-in and network effects
Users get
  • A free service — for now
  • Little say over terms or data
  • No share of the value created
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Dependence
When development runs on rented rails
NGOs and governments increasingly build on platforms they do not control — cloud servers, messaging apps, ad systems. A change in pricing, policy or access can disrupt critical services overnight.
Ask before you build: what happens to our beneficiaries if this platform changes its rules, raises its price, or shuts our account?
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Sovereignty
Data sovereignty & local alternatives
Data sovereignty is the idea that a community or country should have meaningful control over data about its people. Public digital infrastructure and open-source tools are part of the response.
The goal is not autarky but agency: the ability to set terms, keep critical data local, and not be wholly dependent on a single foreign firm.
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Practitioner Stance
Using platforms with eyes open
  • Prefer open, portable formats so you are not locked in
  • Keep your own copy of essential data, off the platform
  • Read the data terms — what does the platform take from your users?
  • Favour public or community infrastructure for critical services where viable
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09
Section Nine
Misinformation & Online Harms
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The Landscape
Information disorder in everyday life
False and misleading content spreads through the same channels people rely on for news, health advice and family contact. In South Asia, much of it flows through WhatsApp and other messaging apps.
Misinformation
False, but shared without intent to harm — a worried relative forwarding a 'cure'.
Disinformation
False and spread deliberately — to deceive, profit or incite.
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Why It Spreads
The mechanics of a rumour
Encrypted, closed groups make forwarded messages feel personal and trustworthy — they come from family, not a stranger. There is no visible source, no correction, and outrage travels faster than fact.
A message from your uncle's group carries the weight of your uncle, even when its content is false. Trust in the messenger launders the message.
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Real Harm
When rumours turn to violence
Viral rumours — for example false 'child-kidnapper' messages forwarded on WhatsApp — have been linked to mob violence and killings in India. Online falsehood becomes offline harm.
This is not a debate about opinions. Misinformation at scale can get people hurt and killed, and inflame communal tension.
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Targeting the Poor
Scams that prey on the vulnerable
  • Fake 'KYC update' messages that steal banking OTPs
  • Bogus government-scheme and lottery messages demanding a fee
  • Loan-app traps with hidden charges and harassment
  • Phishing aimed at first-time, low-literacy internet users
New users with little digital experience and thin financial buffers are prime targets — and lose the most when caught.
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Health & Elections
Where false information does the most damage
Health
Vaccine rumours, fake cures and dangerous remedies cost lives, as seen vividly during COVID-19.
Democracy
Coordinated falsehoods can distort elections, inflame division and erode trust in institutions.
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Moderation
Content moderation in Indian languages
Platforms moderate far better in English than in Hindi, Tamil, Bengali, Marathi and dozens of other languages. Harmful content in under-resourced languages and code-mixed scripts often slips through.
The places with the most users and the highest real-world stakes frequently get the least moderation investment. Language is a safety gap.
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Tension
Moderation vs free expression
Removing harmful content and protecting free speech pull against each other. Over-removal silences dissent and minorities; under-removal lets harm spread. Neither side is automatically right.
Beware moderation powers framed as safety that become tools to suppress legitimate criticism. Who decides, and who can appeal, matters as much as the rule.
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What Helps
Responses that actually work
  • Digital literacy: teach people to pause, check and verify before forwarding
  • Friction: limits on mass-forwarding slow viral spread
  • Local fact-checking in the languages people actually use
  • Trusted messengers — community voices counter rumours best
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10
Section Ten
Responsible Design & Principles
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The Shift
Ethics at the start, not the audit
Most digital harm is designed in early and discovered late. Responsible design moves ethical questions to the first meeting, where they are cheap to fix — not the launch review, where they are not.
You cannot bolt ethics on at the end. By then the data is collected, the model is trained and the exclusions are baked in.
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By Design
Privacy & ethics by design
Privacy by design
Building privacy protections into a system's architecture and defaults from the outset — rather than adding them later as an afterthought or an opt-in.
  • Privacy-protective defaults, not opt-out traps
  • Collect the minimum; delete when done
  • Security and access controls built in from day one
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A Real Framework
The Principles for Digital Development
The Principles for Digital Development are a widely endorsed set of guidelines for using technology in development work — a real, sector-wide framework, not a slogan.
  • Design with the user; understand the existing ecosystem
  • Design for scale and sustainability
  • Be data-driven; use open standards and reusable tools
  • Address privacy & security; be collaborative
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The Rule
Do no (digital) harm
The humanitarian principle of do no harm applies fully to data and technology. Before deploying, ask how the system could be misused — and who would be hurt if it were.
01
Map who could be harmed
→
02
Map how data could be misused
→
03
Design the safeguard & the fallback
→
04
Decide: is the benefit worth the residual risk?
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Core Practice
Data minimisation as a habit
The single most protective habit is to collect less. Every field you do not gather is a breach that cannot happen and a tool that cannot be turned against people later.
Collect less
fewer fields, less risk, less liability
Keep less
delete on a schedule; don't hoard 'just in case'
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Participation
Design with, not for
The people affected by a system know its failure modes best. Involving them in design surfaces exclusions, language gaps and risks that outsiders simply cannot see.
Nothing about us without us. Co-design is not a courtesy — it is how you find the harms before they reach a million people.
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Accountability
Build in redress from the start
  • A clear, reachable way to contest an automated decision
  • A human who can override the system when it is wrong
  • Plain-language notice of what data is held and why
  • An exit: people can correct, delete, or refuse
A system with no appeal route is not efficient — it is unaccountable.
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11
Section Eleven
Governance, Regulation & Practice
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Who Governs
The layers that hold systems to account
01
LAW: the DPDP Act & constitutional rights
→
02
REGULATOR: a data-protection authority
→
03
ORGANISATION: your own policies & reviews
→
04
PRACTITIONER: the daily choices you make
Governance is not only the regulator's job. The last and most frequent line of defence is the person designing the form.
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The Authority
Data protection authorities
The DPDP Act establishes a Data Protection Board of India to handle breaches and grievances. Globally, independent data protection authorities enforce rules, investigate complaints and impose penalties.
A law is only as strong as the body that enforces it. Watch for independence, resources and real power to act — not just words on paper.
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Rights
The rights people hold over their data
  • Access: know what data is held about you
  • Correction: fix what is wrong
  • Erasure: have data deleted when no longer needed
  • Grievance: complain and seek redress
Help the communities you work with understand these rights. A right nobody knows about protects nobody.
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Checklist
An organisation's digital-ethics checklist
AreaAsk before you deploy
PurposeIs each data field truly necessary?
ConsentIs it free, informed, specific, revocable?
InclusionWho does this design exclude — and is there a fallback?
BiasHave we tested outcomes across groups?
SecurityWho can access this, and is it protected?
RedressCan a person contest a decision and reach a human?
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Procurement
Ethics when you buy, not just when you build
Most organisations buy or adopt tools rather than build them. The ethics questions still apply — ask the vendor, and make the answers part of the contract.
  • Where is data stored, and who can access it?
  • Has the tool been tested for bias and accessibility?
  • What happens to our data if we leave the vendor?
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The Takeaways
If you remember five things
  • Behind every record is a person — usually with little power to refuse
  • Collect less — minimisation is the strongest safeguard
  • Design for the person the system fails, not the headline coverage
  • Bias is inherited from data and proxies — audit, don't assume
  • Keep a human accountable and an appeal route open
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Keep Learning
Further reading & companion courses
  • Weapons of Math Destruction — Cathy O'Neil (algorithmic harm)
  • The Costs of Connection — Couldry & Mejias (data colonialism)
  • Automating Inequality — Virginia Eubanks (welfare & tech)
  • Data Feminism — D'Ignazio & Klein (power and data)
  • The Principles for Digital Development — the sector framework
Pair this deck with ImpactMojo's Data Literacy, Research Ethics and AI & Society 101 courses.
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Digital Ethics 101 · Complete
Build it like a
person is on the other side.
Every database, model and app in development touches someone with little power to refuse it. Ask who benefits, who is excluded, and who can contest the decision — before you deploy. Explore the rest of the ImpactMojo 101 Series, free forever.
More 101 CoursesExplore ImpactMojoDataverse
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