Q 815.  

When AI agents make or recommend decisions, users often want to know why.

Too little explanation can create distrust, but too much detail can overwhelm or confuse.

The right level of transparency depends on the context, audience, and impact of the decision.

 

Think of one process in your domain where AI provides or influences decisions.

How much explanation should it offer — and in what form (summary, rationale, confidence level, or evidence)?

Where would you draw the line between clarity and cognitive overload?

 

⚠️ Note: Any answer that is generic or does not connect with a specific, relevant process will not be approved.

 

🏆 The best answer will be selected on the basis of:

 

• Relevance and clarity of the chosen scenario
• Practicality and creativity in the explainability approach
• Balance between user trust and usability

 

 

 

Note for website visitors -

• This platform hosts two weekly questions, one on Monday and the other on Thursday. 
• All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. 
• To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/.
• The question will be open until Monday or Thursday at 5 PM Indian Standard Time, depending on the launch day. 
• Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be considered for winner selection. 
• If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. 
• All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honourable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term.

• Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error-prone because our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/

• We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be considered for winner selection.

Domain Selected :- Quality Assurance in Manufacturing

 

How Much Should AI Explain Its Decisions?

In Quality, trust comes from understanding why a decision was made.
AI now supports critical functions like internal non-conformance handling, customer complaints handling, explanation levels must match the audience.
Too little information creates doubt and Too much creates unnecessary confusion.
The right balance depends on risk, audience and impact.

---

Process: Non-Conformance Handling & Customer Complaints

In our manufacturing process, AI reviews NCR data, tool change logs and past dispositions to suggest the next step — for example:

“Defect matches 91% similarity with PN-1072. Likely cause: tool wear after 12 hrs. Past disposition: rework; success rate = 85%.”

That’s useful, but each level in the organization needs a different depth of explanation to act confidently without being overloaded.

---

How Much and in What Form

1️⃣ Operators & Inspectors – Quick Clarity

They need to act fast, not analyze data.
AI should show:

• Recommendation: “Rework suggested.”
• Confidence: 82%
• Top factor: “Tool beyond set life.”
• Visual clue: 🟢🟠🔴 traffic-light status.
  Simple, direct, and actionable — no background theory.

---

2️⃣ QA Engineers – Complaint RCA & Structured Rationale

QA Engineers validate AI decisions so they need reasoning, not just raw data / rework decision.
AI should provide:

• Key co-relatable variables (tool wear, Tool life not monitored, coolant flow, setup change)
• Top three likely causes with probabilities (basis historic data from NCR data base)
• Linked past NCRs and results
  It should explain like a junior engineer: factual, logical and concise.

---

3️⃣ Auditors / Customers – Full Traceability

For customer complaints or external audit cases, explanations must show back up evidence and accountability:

• Root cause Analysis flow (“Tool wear → setup drift → scratch”)
• Parameter trends and charts.
• Confidence range and risk level
  This builds external trust and transparency.

---

4️⃣ Top Management – Strategic Insight

Executives do not need micro level data; they need impact and direction.
AI should present summarized aggregated insights for review such as:

• KPI dashboards: On-Time Customer Complaint Containment actions %, on-time Complaint RCA closure cycle time %, Customer complaints trend, Cost Of Poor Quality (COPQ) trend and supplier PPM.
• Root cause Buckets: “In-correct Assembly 28% | Parts Missing / Wrong part used 17% | Wrong Labelling 12%”.
• Forecasts: “If trend continues, Customer ‘B’ PPM may breach target by Week 46.”

This level tells leadership what matters: risk, cost, and customer trust — not algorithm details.

---

Drawing the Line: Clarity vs. Overload

Audience	Format	Detail	Purpose
Operator / Inspector	Summary + Confidence Level	Low	Act quickly
QA Engineer	Rationale + Evidence	Medium	Validate logic
Auditor / Customer	Full traceability	High	Justify decisions
Top Management	KPI + Risk summary	Strategic	Guide decisions

 

AI must explain just enough for taking informed action,  not too much that user drowns in data.

---

The Balance

AI should communicate decisions like a skilled engineer:

• What it recommends,
• Why it believes so,
• How sure it is and
• What evidence supports it.

Beyond that, detail becomes distraction.
When AI explains its reasoning at the right depth for each level — from the shop floor to the boardroom — it builds confidence, speedy decisions and turns data into trust.

 

A credit card application approval process using Artificial Intelligence which is in-line with Group 1 Capstone Project scenario is a perfect example wherein too little explanation can create distrust, but too much detail can overwhelm or confuse. Actually, the right level of transparency depends on various factors like:

1.     Audience: Who is accessing or consuming this information – A banking officer who needs to authorize/deny a credit card or the applicant.

2.     Purpose / context: To be able to take rational unbiased decision by looking at insights generated by AI or just to know why an application got rejected & next course of action.

In order to effectively maximize the benefits of AI-assisted credit card application approval process we need to draw the line between clarity and cognitive overload. The level details,  explainability directly impacts trust, compliance, and customer satisfaction.

 

Process:

An AI decision engine screens credit card applications using parameters like income stability, existing credit utilization, repayment behaviour, and fraud risk. It recommends approve, decline, or manual review outcomes.

 

How much explanation to offer — and in what form:

1. For Approval Authority: Bank Credit Officers (Internal Users):
They need just enough information with some key indicators for decision transparency with actionable detail. They don’t need to understand which model is running in the background, which algorithms are used to come up with decision as its reliability has already been tested before it was deployed for this work.
The AI should display:

• Reasoning or Rationale summary:
  “Application declined due to high credit utilization (93%) and three recent late payments.”
• Confidence level:
  “Confidence: 95% (based on 5,000 similar profiles).”
• Evidence snapshot with ONLY VALUES & no explanation or justification:
  Key indicators such as income-to-debt ratio, credit score, and repayment trends.

This allows officers to justify or override decisions quickly while meeting audit and compliance needs.

 

2. For Credit Card Applicants (External Users):
Customers only need a concise and fair explanation that is easy to understand without confusing them with too many industry jargons, indicators and model explanations.
Example:
“Dear “Applicant” your application cannot be approved at this time because your recent current credit obligations are high relative to your income. You may reapply after six months if your credit balance improves.”

No model terms, data weights, or risk scores — just a simple, non-technical reason and guidance on next steps.

 

Where to draw the line between clarity and overload:

• Include: clear rationale , key factor summary , confidence for internal users.
• Exclude: algorithm parameters or detailed feature importance charts.
• For customers: stay with a single reason, plain language, and assurance of fairness/human review.

This achieves trust without technical noise — clear enough for decision accountability, simple enough for customer understanding, and compliant with fair-lending disclosure norms.

 

Consider an AI tool in the publishing world that helps decide which manuscripts get forwarded for peer review. It looks at things like how relevant the manuscript is, its originality, the quality of the writing, its potential for citations, and whether it fits the journal's focus.

 

When the AI suggests something—like turning down a submission—the explanation it gives has to hit that sweet spot: clear enough to understand without burying people in details.

 

At the very least, it must provide a brief summary of the call along with a confidence score. That way, editors get the gist right away and know how sure the AI is feeling. For everyday calls, that's usually plenty.

However, rejecting a good submission could be a game-changer; the AI should provide the reasoning behind it, including the main reasons, such as "This scored low on novelty and had poor citation potential." And if someone is pushing back, it can pull up supporting information, maybe topic breakdowns or citation stats.

To avoid overwhelming users, make the information unfold in stages. Start with a summary and confidence, then let editors delve deeper into the why and the proof if they wish. It will give users control over the level of detail they want.

 

The level of explanation should also depend on the user's role. Editors need concise, actionable insights with optional depth. Authors may need an apparent reason for rejection, but not the full technical breakdown. Reviewers might benefit from understanding why a manuscript was flagged for review.

 

In the end, figuring out clarity versus overload comes down to three things:

1. How much the decision matters,

2. What hat is the user wearing, and

3. How tangled the explanation gets.

 

True transparency is not data overload—it is earning trust by providing information that is smart, relevant, and tailored to the user.

The required level of AI decision explanation depends on the context , the stakeholder involved and the user need for understanding and trust, where criticality of decision is low, a simple what and why is enough but for critical decision where cost impact is very high , we need detailed reasoning. AI explanation helps to build Trust & Confidence , Mitigating RISK , Faster end result .

For example , consider the Cold rolling steel strip manufacturing lifecycle (refer attached slide#1) , which is highly complex , where multiple stakeholders are involved, multiple machines are used to thin a steel coil to a precise thickness with right hardness and surface flatness at high speeds. Where AI can be used to make real time critical decision to improve efficiency and performance. AI systems often consider below input variable factors.

• Rolling force,
• Lubrication
• Speed adjustments
• Temperature control

Just imaging that AI predicts that the rolling force should increase by 6% during the final pass. Machine operators see this as a recommendation but don’t know if it’s due to material hardness, temperature shift , or sensor error. Without AI explanation , the risk is blind trust without understanding which can result in critical defects or equipment damage.

But when AI explain its decision through dashboard

1.      at Operator level (Actionable view) – (what?)

-->Increase force by 5% to maintain thickness tolerance.

 

2.      at Supervisor level (How?)

--> Show top 3 contributing factors like hardness, temperature, speed etc.

-->And new message on other influential feature to concern operator

 

3.      at Engineer level  (Why?) -    Dashboard chart showing feature impact analysis,

--> Maintenance History of all related machines.

--> Historical Trend comparison of all related machine performance on similar production line.

 

4.      at Manager Level (Trust and impact view)

--> Confidence scores on decision and related risk indicators, like If force is not increased by 6%, predicted deviation - 0.15 mm.

--> From a business impact angle, the proposed adjustment helps prevent a potential scrap loss of INR. 20Cr and maintain ISO quality compliance.

There is no single standard for how much AI should explain but this explainability is needed due to legal, ethical, trust and risk mitigation perspective.

AI explanations on its decision can be grouped into three parts, like

·       Prediction Accuracy – to show how effective, AI decision is,

·       Traceability – to confirm that decisions are based on valid data and processes,

·       Decision Understanding – to help user understand the reasoning behind the AI’s decisions.

Ultimately the goal is to provide meaningful transparency, providing the right amount of detail to the right user at the right time to enable an effective decision-making process.

 

Winner – Adil Khan

Adil’s Manufacturing QA framework set the gold standard for AI explainability — clear depth by user role, practical formats, and an elegant balance between clarity and overload.

 

Runner-up – Shashi Prakash

His Credit Card Approval example achieved perfect fairness and compliance — detailed for officers, simple for customers.

 

Special Mention – Sanjib Ghosh

Sanjib’s Cold Rolling Steel Manufacturing case, supported by his visual, demonstrated world-class industrial explainability with four-tier clarity and measurable transparency.

 

Approved – Manik Sood for a well-structured Publishing example using staged explanation depth.